Harnessing Infant Cry for swift, cost-effective Diagnosis of Perinatal Asphyxia in low-resource settings

TL;DR

This paper presents a machine learning approach using infant cry analysis to enable quick, affordable diagnosis of perinatal asphyxia in low-resource settings, potentially reducing infant mortality.

Contribution

It introduces a support vector machine-based system for classifying infant cries to detect asphyxia, addressing diagnostic challenges in resource-limited environments.

Findings

Achieved up to 88.85% prediction accuracy in laboratory tests.

Demonstrated potential for low-cost, rapid diagnosis of perinatal asphyxia.

Supports global health goals by improving early detection in developing countries.

Abstract

Perinatal Asphyxia is one of the top three causes of infant mortality in developing countries, resulting to the death of about 1.2 million newborns every year. At its early stages, the presence of asphyxia cannot be conclusively determined visually or via physical examination, but by medical diagnosis. In resource-poor settings, where skilled attendance at birth is a luxury, most cases only get detected when the damaging consequences begin to manifest or worse still, after death of the affected infant. In this project, we explored the approach of machine learning in developing a low-cost diagnostic solution. We designed 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. Our prototype has been tested in a laboratory setting to give prediction accuracy of up to 88.85%. If higher accuracies can be obtained, this research may be a key contributor to the 4th Millennium Development Goal (MDG) of reducing mortality in under-five children.