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      Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

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          Abstract

          Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                November 2013
                15 November 2013
                : 13
                : 11
                : 15613-15632
                Affiliations
                [1 ] Electrical Engineering Course, Federal University of Piauí (UFPI), 64049-550, Teresina, Piauí, Brazil; E-Mails: josemenezesjr@ 123456ufpi.edu.br (J.M.P.M.J.); otacilio@ 123456ufpi.edu.br (O.M.A.)
                [2 ] Department of Electrical Engineering, Federal University of Ceará (UFC), 60020-181, Fortaleza, Ceará, Brazil; E-Mail: alberto.alexandre@ 123456gmail.com
                [3 ] Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (UFRN), 59078-900, Natal, Rio Grande do Norte, Brazil; E-Mail: meneghet@ 123456dca.ufrn.br
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: medeiros_eng@ 123456yahoo.com.br ; Tel.: +55-86-3237-1555.
                Article
                sensors-13-15613
                10.3390/s131115613
                3871086
                24248278
                © 2013 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

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