GPU based Deep Learning to Detect Asphyxia in Neonates

Abstract

Throughout the years, some countries have not seen any reduction in the death rate of neonates. Neonate refers to a baby within its first four weeks of life. Birth asphyxia is one of the three noteworthy reasons for neonatal deaths globally. A birth injury is demonstrative of some kind of mistake that changed an ordinary delivery into a traumatic ordeal for the newborn child (and mother). Perinatal asphyxia, or neonatal asphyxia, is a birth injury where a child doesn't inhale regularly before, during or after birth. Asphyxia is a condition that depicts a diminished or ceased level of oxygen, and the perinatal stage is the period before, during or immediately after birth. At the point when an infant has not been breathing appropriately, there is danger of cerebrum harm and acidosis (a condition when a lot of acid builds up in the blood) which may result in the death of the newborn child if undiscovered or analyzed late. Our project uses machine learning in building up a minimal effort symptomatic arrangement. This paper has composed a machine-based example framework that identifies designs in the voices of known suffocating babies (and typical newborn children) while crying. It then utilizes the created model to predict if the newborn is affected by asphyxia or not. An accuracy of 92% was achieved. It will serve as a valuable apparatus in diminishing death rate everywhere throughout the world if accuracy can be improved.