Temporal issues in the intelligent interpretation of the sleep Apnea syndrome

Abstract

Automation of the medical diagnosis of the Sleep Apnea Syndrome (SAS) requires an intelligent analysis of the pneumological and neurophysiological signals of the patient that combines both conventional and Artificial Intelligence techniques in order to detect respiratory abnormalities and construct a hypnogram for the patient, and a process of temporal fusion and correlation between the signals for both a correct classification of the apneic events within a sleep stage framework, and to explain the occurrence of abnormal sleep patterns as a consequence of these events. In this article, the most important aspects of the analysis and information integration processes are described and the preliminary validation results obtained are discussed.