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

Sensors

José Medeiros de Araújo Júnior¹  José Maria Pires de Menezes Júnior¹  Alberto Alexandre Moura de Albuquerque²  Otacílio da Mota Almeida¹ 

[1] Electrical Engineering Course, Federal University of Piauí (UFPI), 64049-550, Teresina, Piauí, Brazil; E-Mails:;Department of Electrical Engineering, Federal University of Ceará (UFC), 60020-181, Fortaleza, Ceará, Brazil; E-Mail:

关键词: sensors;    inferential neural network;    nonlinear identification;    neonatal incubator;    certification procedure;   

DOI  :  10.3390/s131115613

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.