Fetal yawning is of interest because of its clinical, developmental and theoretical implications. However, the methodological challenges of identifying yawns from ultrasonographic scans have not been systematically addressed. We report two studies that examined the temporal dynamics of yawning in preterm neonates comparable in developmental level to fetuses observed in ultrasound studies (about 31 weeks PMA). In Study 1 we tested the reliability and construct validity of the only quantitative measure for identifying fetal yawns in the literature, by comparing its scores with a more detailed behavioral coding system (The System for Coding Perinatal Behavior, SCPB) adapted from the comprehensive, anatomically based Facial Action Coding System for Infants and Young Children (Baby FACS). The previously published measure yielded good reliability but poor specificity, resulting in over-representation of yawns. In Study 2 we developed and tested a new machine learning system based on support vector machines (SVM) for identifying yawns. The system displayed excellent specificity and sensitivity, proving it to be a reliable and valid tool for identifying yawns in fetuses and neonates. This achievement represents a first step towards a fully automated system for identifying yawns in the perinatal period.

Identifying fetal yawns based on temporal dynamics of mouth openings: A preterm neonate model using support vector machines (SVMs)

Menin, Damiano
Primo
Conceptualization
;
Dondi, Marco
Ultimo
Project Administration
2019

Abstract

Fetal yawning is of interest because of its clinical, developmental and theoretical implications. However, the methodological challenges of identifying yawns from ultrasonographic scans have not been systematically addressed. We report two studies that examined the temporal dynamics of yawning in preterm neonates comparable in developmental level to fetuses observed in ultrasound studies (about 31 weeks PMA). In Study 1 we tested the reliability and construct validity of the only quantitative measure for identifying fetal yawns in the literature, by comparing its scores with a more detailed behavioral coding system (The System for Coding Perinatal Behavior, SCPB) adapted from the comprehensive, anatomically based Facial Action Coding System for Infants and Young Children (Baby FACS). The previously published measure yielded good reliability but poor specificity, resulting in over-representation of yawns. In Study 2 we developed and tested a new machine learning system based on support vector machines (SVM) for identifying yawns. The system displayed excellent specificity and sensitivity, proving it to be a reliable and valid tool for identifying yawns in fetuses and neonates. This achievement represents a first step towards a fully automated system for identifying yawns in the perinatal period.
Menin, Damiano; Costabile, Angela; Tenuta, Flaviana; Oster, Harriet; Dondi, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2412486
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