Intelligent diagnosis of sleep apnea syndrome

• Published in: IEEE engineering in medicine and biology magazine Vol. 23; no. 2; pp. 72 - 81
• Main Authors: Cabrero-Canosa, M., Hernandez-Pereira, E., Moret-Bonillo, V.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Automatic programming; Biomedical measurements; Data analysis; Decision Support Systems, Clinical; Diagnosis, Computer-Assisted - methods; Humans; Information analysis; Medical diagnostic imaging; Pilot Projects; Polysomnography - methods; Prognosis; Reproducibility of Results; Sensitivity and Specificity; Signal analysis; Sleep apnea; Sleep Apnea Syndromes - diagnosis; Software; Synthetic aperture sonar; Time measurement
• ISSN: 0739-5175; 1937-4186
• DOI: 10.1109/MEMB.2004.1310978

An effective diagnosis of the sleep apnea syndrome (SAS) is based on a contextual analysis of the patient's polysomnograph, consisting of simultaneously recording electrophysiological and pneumological signals during a night's sleep. Currently, the prevalence of this disorder has caused an increase in the demand for specialist clinical assistance and sleep units. As in other areas of medicine, the volume of clinical data that has to be processed is enormous, which justifies the construction of computerized decisionmaking tools that partially automate these routine tasks. Our system, SAMOA, belongs to this category of help tools, being an automatic SAS diagnostic system that incorporates both conventional programming and artificial intelligence techniques. This article describes the most important aspects of the temporal data management in the different analysis processes and the final correlation of all the symbolic information generated by the different cooperative modules.