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The obstructive sleep apnea-hypopnea (OSAH) syndrome is a very common andfrequently undiagnosed sleep disorder. It is characterized by repeated eventsof partial (hypopnea) or total (apnea) obstruction of the upper airway whilesleeping. This study makes use of a previously developed method called DAS-KSVDfor multiclass structured dictionary learning to automatically detectindividual events of apnea and hypopnea using only blood oxygen saturationsignals. The method uses a combined discriminant measure which is capable ofefficiently quantifying the degree of discriminability of each one of the atomsin a dictionary. DAS-KSVD was applied to detect and classify apnea and hypopneaevents from signals obtained from the Sleep Heart Health Study database. Formoderate to severe OSAH screening, a receiver operating characteristic curveanalysis of the results shows an area under the curve of 0.957 and diagnosticsensitivity and specificity of 87.56% and 88.32%, respectively. These resultsrepresent improvements as compared to most state-of-the-art procedures. Hence,the method could be used for screening OSAH syndrome more reliably andconveniently, using only a pulse oximeter.

Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals