Intelligent Approach for Analysis of Respiratory Signals and Oxygen Saturation in the Sleep Apnea/Hypopnea Syndrome

Vicente Moret-Bonillo*, 1, 2, Diego Alvarez-Estévez 1, Angel Fernández-Leal 1, Elena Hernández-Pereira 1
1 Laboratory for Research and Development in Artificial Intelligence (LIDIA), University of La Coruña, 15071. Spain
2 Research Centre on Information and Communication Technologies (CITIC), University of La Coruña, 15071. Spain

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© Moret-Bonillo et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Laboratory for Research and Development in Artificial Intelligence (LIDIA), University of La Coruña, 15071. Spain; Tel: +34 981 167 000 Ext: 1248-5519-1305; Fax: +34 981 167 160; E-mail:


This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.

Keywords: Artificial Intelligence in Medicine, Decision Support Systems, Fuzzy Logic, Intelligent Monitoring, Signal Processing, Sleep Apneas, Temporal Reasoning..