Application of Recurrence Quantification Analysis to Automatically Estimate Infant Sleep States Using a Single Channel of Respiratory Data

Med Biol Eng Comput. 2012 Aug;50(8):851-65. doi: 10.1007/s11517-012-0918-4. Epub 2012 May 22.


Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Diagnosis, Computer-Assisted / methods*
  • Female
  • Humans
  • Infant
  • Infant, Newborn
  • Male
  • Pattern Recognition, Automated / methods*
  • Plethysmography, Whole Body / methods*
  • Polysomnography / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sleep Stages / physiology*