Automated preterm infant sleep staging using capacitive electrocardiography

Jan Werth (Corresponding author), Aline Serteyn, Peter Andriessen, Ronald Aarts, Xi Long

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Abstract

Objective: To date, mainly obtrusive methods (e.g., adhesive electrodes in electroencephalography or electrocardiography) are necessary to determine the preterm infant sleep states. As any obtrusive measure should be avoided in preterm infants because of their immature skin development, we investigated the possibility of automated sleep staging using electrocardiograph signals from non-adhesive capacitive electrocardiography.
Approach: Capacitive electrocardiography data from eight different patients with a mean gestational age of 30 ± 2.5 weeks are compared to manually annotated reference signals from classic adhesive electrodes. The sleep annotations were performed by two trained observers based on behavioral observations.
Main results: Based on these annotations, classification performance of the preterm infant active and quiet sleep states, based on capacitive electrocardiography signals, showed a kappa value of 0.56 ± 0.20. Adding wake and caretaking into the classification, a performance of Kappa 0.44 ± 0.21 was achieved. In-between sleep state performance showed a classification performance of Kappa 0.36 ± 0.12. Lastly, a performance for all sleep states of Kappa 0.35 ± 0.17 was attained.
Significance: capacitive electrocardiography signals can be utilized to classify the central preterm infant sleep states, active and quiet sleep. With further research based on our results, automated classification of sleep states can become an essential instrument in the future intensive neonatal care for continuous brain maturation monitoring. Especially, being able to use capacitive electrocardiography for continuous monitoring is a significant contributor to reducing disruption and harm for this extreme fragile patient group.
Original languageEnglish
Article number055003
Number of pages14
JournalPhysiological Measurement
Volume40
Issue number5
Early online date21 Mar 2019
DOIs
Publication statusPublished - 4 Jun 2019

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Keywords

  • automated sleep classification
  • capacitive ECG
  • classic ECG
  • machine learning
  • preterm infants
  • unobtrusive

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