title: Assessment and certification of neonatal incubator sensors through an inferential neural network.
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.
authors.affiliation: Electrical Engineering Course, Federal University of Piauí (UFPI), 64049-550, Teresina, Piauí, Brazil. eng@yahoo.com.br.
authors.forename: José Medeiros
authors.initials: JM
authors.lastname: de Araújo
authors.forename: José Maria Pires
authors.initials: JM
authors.lastname: de Menezes
authors.forename: Alberto Alexandre
authors.initials: AA
authors.lastname: Moura de Albuquerque
authors.forename: Otacílio
authors.initials: O
authors.lastname: da Mota Almeida
authors.forename: Fábio Meneghetti
authors.initials: FM
authors.lastname: Ugulino de Araújo
mesh_terms: D015374:Biosensing Techniques / Q000592:standards*; D002568:Certification; D004867:Equipment Design; D006801:Humans; D007186:Incubators, Infant; D007231:Infant, Newborn; D016571:Neural Networks, Computer