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Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological

Harpreet Singh1, Satoshi Kusuda2, Ryan M McAdams3

  • 1Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore.

Children (Basel, Switzerland)
|December 30, 2020
PubMed
Summary

Machine learning accurately identifies neonatal care manipulations using video and physiological data. This technology helps document interventions and their impact on infant heart rate and oxygen saturation in the NICU.

Keywords:
CNNIoTLSTMelectronic medical recordsmachine learningneonatal intensive care unitsphysiological deviationsphysiological parametersstreaming servervideo monitoring

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Area of Science:

  • Neonatal Intensive Care Unit (NICU) research
  • Machine learning applications in healthcare
  • Physiological monitoring in neonates

Background:

  • Neonatal care involves frequent manipulations that can affect infant well-being.
  • Objective monitoring of these manipulations and their physiological impact is crucial.
  • Current methods for tracking neonatal interventions are often manual and subjective.

Purpose of the Study:

  • To develop and validate a machine learning system for automatic recognition of neonatal manipulations.
  • To assess the impact of different neonatal manipulations on physiological parameters like heart rate and oxygen saturation.
  • To investigate the feasibility of using deep learning for objective documentation of NICU care.

Main Methods:

  • A retrospective observational study utilizing video and physiological data (HR, SpO2) from ten neonates in NICUs.
  • Development of a deep learning system combining Inception-v3 CNN and LSTM for manipulation classification.
  • Validation using a leave-one-out strategy with 8-second video clips of manipulation activity.

Main Results:

  • The machine learning system achieved 95% accuracy in training and 85% in validation.
  • Diaper changes significantly altered HR and SpO2 in preterm infants (<32 weeks gestation).
  • Patting and tube feeding were associated with significant HR changes in more mature infants (≥32 weeks gestation).

Conclusions:

  • The presented deep learning system accurately classifies and documents neonatal manipulations.
  • NICU care interventions demonstrably impact neonatal physiological parameters.
  • This technology offers potential for objective assessment and improved understanding of neonatal care practices.