IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 3,
pp. 796-803, March 2020
Birth asphyxia is a major newborn mortality problem in low-resource
countries. International guideline provides treatment recommendations; however,
the importance and effect of the different treatments are not fully explored.
The available data is collected in Tanzania, during newborn resuscitation, for
analysis of the resuscitation activities and the response of the newborn. An
important step in the analysis is to create activity timelines of the episodes,
where activities include ventilation, suction, stimulation etc. Methods: The
available recordings are noisy real-world videos with large variations. We
propose a two-step process in order to detect activities possibly overlapping
in time. The first step is to detect and track the relevant objects, like
bag-mask resuscitator, heart rate sensors etc., and the second step is to use
this information to recognize the resuscitation activities. The topic of this
paper is the first step, and the object detection and tracking are based on
convolutional neural networks followed by post processing. Results: The
performance of the object detection during activities were 96.97 %
(ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction)
on a test set of 20 videos. The system also estimate the number of health care
providers present with a performance of 71.16 %. Conclusion: The proposed
object detection and tracking system provides promising results in noisy
newborn resuscitation videos. Significance: This is the first step in a
thorough analysis of newborn resuscitation episodes, which could provide
important insight about the importance and effect of different newborn
resuscitation activities
Øyvind Meinich-Bache, Kjersti Engan, Ivar Austvoll, Trygve Eftestøl, Helge Myklebust, Ladislaus Blacy Yarrot, Hussein Kidanto, Hege Ersdal
2023-03-14
