Resumen

ESCOBEDO BECEIRO, Daniel Isac et al. How to classify newborn cry through a supervised neural network. RCIM [online]. 2022, vol.14, n.1  Epub 01-Jun-2022. ISSN 1684-1859.

Cry from newborn (0-28 days) is a way of communication for the interaction with surrounding world. Infant cry researches provide information that correlate among cries acoustic features with pathologies. It has been demonstrated that the infant cry is able to reflect child neurophysiology integrity and give meaning from newborn interaction with environment, also cognitive and social development from child. This contribution shows how to classify the cry of neonates with hypoxia and of a control group, into normal or pathological, through a supervised artificial neural network. Network implementation makes use of MATLAB® platform possibilities. Design and structuring of network take into consideration aspects as training algorithm, iterations, tests and classification intervals. All these referred aspects give as result an architectural, topology and functionalities from neural network able to classify cry in generalization stage offering good outcome. Different methods are applied in this paper as selection of cases, acoustic methods in order to obtain quantitative parameters from cry signals (in time, intensity and frequency domain). Methods related with design, implementation and validation (diagnostic test) of an artificial neural network able to carry out the goal of this paper (classification of cry) are used. With accuracy results in cry classification about 90 %, authors get ready conditions for an informatic solution (with addition of interface for data base interaction) for help as a non-invasive complement to medical diagnosis using cry from neonate induced by pain.

Palabras clave : infant cry analysis; cry classification; artificial neural network; supervised neural network; backpropagation.

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