Quantifying Movement in Preterm Infants Using Photoplethysmography.


Journal

Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512

Informations de publication

Date de publication:
Feb 2019
Historique:
received: 13 05 2018
accepted: 18 09 2018
pubmed: 27 9 2018
medline: 11 5 2019
entrez: 27 9 2018
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30255214
doi: 10.1007/s10439-018-02135-7
pii: 10.1007/s10439-018-02135-7
pmc: PMC6344309
mid: NIHMS1508053
doi:

Types de publication

Clinical Trial Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

646-658

Subventions

Organisme : NHLBI NIH HHS
ID : U01 HL133536
Pays : United States
Organisme : NICHD NIH HHS
ID : R21 HD089731
Pays : United States
Organisme : NIGMS NIH HHS
ID : GM104987
Pays : United States
Organisme : Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : HD089731
Organisme : NIGMS NIH HHS
ID : R01 GM104987
Pays : United States
Organisme : National Science Foundation
ID : 1664815
Organisme : NHLBI NIH HHS
ID : HL133536
Pays : United States

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Auteurs

Ian Zuzarte (I)

Department of Bioengineering, Northeastern University, Boston, MA, USA.

Premananda Indic (P)

Department of Electrical Engineering, University of Texas, Tyler, TX, USA.

Dagmar Sternad (D)

Departments of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, Boston, MA, USA.

David Paydarfar (D)

Department of Neurology, Dell Medical School, and Institute for Computational Engineering and Sciences, The University of Texas, 1701 Trinity St. Stop Z0700, Health Discovery Bldg, 5.708A, Austin, TX, 78712, USA. david.paydarfar@austin.utexas.edu.

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Classifications MeSH