Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

  title={Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia},
  author={Charles C. Onu and Jonathan Lebensold and William L. Hamilton and Doina Precup},
Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every year. The prediction of pathologies affecting newborns based on their cry is thus of significant clinical interest, as it would facilitate the development of accessible, low-cost diagnostic tools\cut{ based on wearables and smartphones}. However, the inadequacy of clinically annotated datasets of infant cries limits progress on this task. This study… 

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Harnessing infant cry for swift, cost-effective diagnosis of Perinatal Asphyxia in low-resource settings

  • Charles C. Onu
  • Medicine
    2014 IEEE Canada International Humanitarian Technology Conference - (IHTC)
  • 2014
A support vector machine-based pattern recognition system that models patterns in the cries of known asphyxiating infants (and normal infants) and then uses the developed model for classification of `new' infants as having asphyxia or not is designed.

A System for the Processing of Infant Cry to Recognize Pathologies in Recently Born Babies with Neural Networks

The design and implementation of the complete system that classifies three different kinds of cries from normal, deaf and asphyxiating infants, of ages from one day up to nine months old are presented.


The results showed significant differences between the cries of the asphyxiated newborn and the healthy infants, which were more abnormal if the infant was found to be neurologically damaged at the check‐up at 2–8 years.

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