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 im- portant aspects of the analysis and information integration processes are described and the preliminary validation results obtained are discussed.
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Cabrero-Canosa, M., Castro-Pereiro, M., Graña-Ramos, M., Hernandez-Pereira, E., Moret-Bonillo, V. (2001). Temporal Issues in the Intelligent Interpretation of the Sleep Apnea Syndrome. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_34
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DOI: https://doi.org/10.1007/3-540-48229-6_34
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