Assessment and certification of neonatal incubator sensors through an inferential neural network

• Published in: Sensors (Basel, Switzerland) Switzerland), 2013-11, Vol.13 (11), p.15613-15632
• Main Authors: de Araújo, José Medeiros, de Menezes, José Maria Pires, Moura de Albuquerque, Alberto Alexandre, da Mota Almeida, Otacílio, Ugulino de Araújo, Fábio Meneghetti
• Format: Article
• Language: English
• Subjects: Biosensing Techniques - standards; Body temperature; Calibration; Certification; certification procedure; Computer engineering; Controllers; Electrical engineering; Equipment Design; Humans; Humidity; Incubators, Infant; Infant, Newborn; inferential neural network; Intensive care; neonatal incubator; Neural networks; Neural Networks (Computer); nonlinear identification; Premature birth; Sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s131115613

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.