NDLI: Fuzzy structural algorithms to identify and characterize apnea and hypopnea episodes

Content Provider	IEEE Xplore Digital Library
Author	Otero, Abraham Felix, Paulo Alvarez, Miguel R. Zamarron, Carlos
Copyright Year	2008
Description	Author affiliation: Division of Respiratory Medicine. University Hospital Complex of Santiago de Compostela, Spain (Zamarron, Carlos) \|\| Department of Software and Knowledge Engineering, University San Pablo CEU, 28668 Madrid, Spain (Otero, Abraham) \|\| Department of Electronics and Computer Science, University of Santiago de Compostela, 15782, Spain (Felix, Paulo; Alvarez, Miguel R.)
Abstract	We present a new automated method to identify apneas and hypopneas and to relate them to the drops in blood oxyhemoglobin saturation that they produce. The algorithm takes advantage of the fuzzy set theory for the representation and manipulation of the vagueness of the medical knowledge on which it is based. Its structural nature allows us to easily modify the morphological detection criteria, and to perform a detailed characterization of the identified events. Based on this proposal, a tool for screening polysomnographic records has been developed. The tool allows the physician to modify the morphological criteria that define apneas, hypopneas, and desaturations as well as to inspect the descriptors that make up their characterization. Using this tool we have analyzed five polysomnographic recordings obtaining an overall correct detection rate of 95%. The false negative rate was 6.6% and the false positive rate was 4.3%.
Starting Page	5242
Ending Page	5245
File Size	190316
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy Set Theory Sleep Apnea-Hypopnea Syndrome (SAHS) Biosignal Processing Structural Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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