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Annals of Biomedical Engineering

, Volume 47, Issue 2, pp 646–658 | Cite as

Quantifying Movement in Preterm Infants Using Photoplethysmography

  • Ian Zuzarte
  • Premananda Indic
  • Dagmar Sternad
  • David PaydarfarEmail author
Article

Abstract

Long-term recordings of movement in preterm infants might reveal important clinical information. However, measurement of movement is limited because of time-consuming and subjective analysis of video or reluctance to attach additional sensors to the infant. We evaluated whether photoplethysmogram (PPG), routinely used for oximetry in preterm infants in the neonatal intensive care unit (NICU), can provide reliable long-term measurements of movement. In 18 infants [mean post-conceptional age (PCA) 31.10 weeks, range 29–34.29 weeks], we designed and tested a wavelet-based algorithm that detects movement signals from the PPG. The algorithm’s performance was optimized relative to subjective assessments of movement using video and accelerometers attached to two limbs and force sensors embedded within the mattress (five infants, three raters). We then applied the optimized algorithm to infants receiving routine care in the NICU without additional sensors. The algorithm revealed a decline in brief movements (< 5 s) with increasing PCA (13 infants, r = − 0.87, p < 0.001, PCA range 27.3–33.9 weeks). Our findings suggest that quantitative relationships between motor activity and clinical outcomes in preterm infants can be studied using routine photoplethysmography.

Keywords

Continuous wavelet transform Movement detection Motor development Preterm movement Photoplethysmography 

Notes

Acknowledgments

The authors thank Courtney Temple and Alan Gee for data collection, Adriell Louis and Hannah Taylor for data annotation; the NICU Staff and Physicians for subject recruitment, and James Niemi and his team at the Wyss Institute for constructing the movement sensor mattress. This work was supported by NSF SCH Grant #1664815, NIH Grants R01-GM104987 and R21-HD089731, and the Wyss Institute at Harvard University.

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Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  • Ian Zuzarte
    • 1
  • Premananda Indic
    • 2
  • Dagmar Sternad
    • 3
  • David Paydarfar
    • 4
    Email author
  1. 1.Department of BioengineeringNortheastern UniversityBostonUSA
  2. 2.Department of Electrical EngineeringUniversity of TexasTylerUSA
  3. 3.Departments of Biology, Electrical and Computer Engineering, and PhysicsNortheastern UniversityBostonUSA
  4. 4.Department of Neurology, Dell Medical School, and Institute for Computational Engineering and SciencesThe University of TexasAustinUSA

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