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dc.contributor.authorFernández-Leal, Ángel
dc.contributor.authorCabrero-Canosa, Mariano
dc.contributor.authorMosquera Rey, Eduardo
dc.contributor.authorMoret-Bonillo, Vicente
dc.date.accessioned2017-02-14T17:26:38Z
dc.date.issued2017-02-15
dc.identifier.citationÁngel Fernández-Leal, Mariano Cabrero-Canosa, Eduardo Mosqueira-Rey, Vicente Moret-Bonillo, A knowledge model for the development of a framework for hypnogram construction, Knowledge-Based Systems, 118 (2017), pp. 140–151es_ES
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.urihttp://hdl.handle.net/2183/18111
dc.descriptionThe final publication is available via http://dx.doi.org/10.1016/j.knosys.2016.11.016es_ES
dc.description.abstract[Abstract] We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for different clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been developed but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not affected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.es_ES
dc.description.sponsorshipXunta de Galicia; GRC2014/035es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN2013-40686-Pes_ES
dc.language.isoenges_ES
dc.publisherElsevier BVes_ES
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S0950705116304737es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectArtificial intelligencees_ES
dc.subjectKnowledge-based systemes_ES
dc.subjectHypnogrames_ES
dc.subjectCommonKADSes_ES
dc.titleA knowledge model for the development of a framework for hypnogram constructiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2019-02-15es_ES
dc.date.embargoLift2019-02-15
UDC.journalTitleKnowledge-Based Systemses_ES
UDC.volume118es_ES
UDC.startPage140es_ES
UDC.endPage151es_ES
dc.identifier.doi10.1016/j.knosys.2016.11.016


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