Temporal Issues in the Intelligent Interpretation of the Sleep Apnea Syndrome
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.
KeywordsPositive Predictive Value Negative Predictive Value Sleep Stage Symbolic Information Intelligent Analysis
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- O. Pacheco. Sistema Assistido por Computador de Classificaçao do Electroence-falograma do Sono e Detecçao de Micro Despertares. PhD thesis, Universidade de Aveiro, Portugal, 1996.Google Scholar
- T. Roth, T. P. Moyles, and R. F. Erlandson. A nonparametric statistical approach to breath segmentation. IEEE Engineering in Medicine and Biology Society, 330–331, 1989.Google Scholar
- F. Sériès, Y. Cormier, and J. La Forge. Role of lung volumes in nocturnal postapneic desaturation. European Respiratory Journal, 2:26–30, 1989.Google Scholar
- S. C. Mishoe. The diagnosis and treatment of sleep apnea syndrome. Respiratory Care, 32(2):183–201, 1987.Google Scholar