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The sleep apnea-hyponea syndrome (SAHS) is a disease characterized by episodes of complete absence (apneas) or signi cant reduction (hypopneas) of breathing during sleep. The apneic events recurrence leads to inadequate gas exchange which causes hypoxia and hypercapnia, resulting in oxygen saturation drops, periodic arousals, as well as sleep fragmentation. As a consequence, SAHS patients are not able to get restful sleep, which a ects their quality of life. Hypersomnolence, decrease in the short-memory function, and depression are some of the daytime symptoms reported by a ected people. Additionally, SAHS has been associated with major cardiovascular and metabolic illnesses such as heart failure, stroke, sudden death, and diabetes. Recently, it has been also associated with an increase in cancer incidence. These SAHS consequences make a fast diagnosis the key action to improve health and quality of life of patients. SAHS is a very prevalent illness, a ecting from 2% to 7% of adult population and up to 6% of children. It is also considered as an underdiagnosed disease, with a growing incidence due to the obesity epidemic present in developed countries. Overnight polysomnography (PSG), conducted in a specialized sleep unit, is the gold standard to diagnose SAHS. However, it is technically complex due to the high number of physiological signals to be recorded, costly due to the need for patient's hospitalization, as well as time-consuming due to the o ine inspection of the recordings, which is required to reach diagnosis. This is obtained by computing the apnea-hypopnea index (AHI) after carefully reviewing of the recorded signals. Moreover, PSG test deprives patients of their natural sleep environment. These drawbacks, the high prevalence of SAHS, as well as the limited availability of specialized facilities, have led to the search for new ways to simplify the diagnostic process. One common approach is the analysis of a reduced set of signals among those involved in full PSG. In this Doctoral Thesis it is posed the automatic analysis of single-channel air ow (AF) as a simple and reliable alternative to PSG. In addition, pattern recognition is proposed as the main approach to conduct an automatic SAHS diagnosis, including binary classi cation (presence or absence of SAHS) as well as determination of SAHS severity degree (multiclass classi cation and AHI estimation by means of regression). We hypothesize that it is possible to reduce the complexity of the SAHS diagnostic process by means of an automatic pattern recognition analysis of AF. Consequently, the general goal of this work is the comprehensive study and assessment of the diagnostic potential of the AF signal as a surrogate for full PSG in SAHS detection. Our methodology is based on three main steps. First, a feature extraction stage is implemented to obtain information of SAHS from single-channel

Pattern recognition applied to airflow recordings to help in sleep Apnea-Hypopnea Syndrome diagnosis