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Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data

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Abstract

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.

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Acknowledgments

We would like to acknowledge the infants and families who participated in the study and the staff at the Mater Children’s Hospital for their dedicated work in collecting the infant sleep study data analysed in this study. The data collection for this research was supported by a grant from the Mater Children’s Hospital Golden Casket Research Fund.

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD, 4072, Australia

    Philip I. Terrill & Stephen J. Wilson

  2. Department Respiratory and Sleep Medicine, The Mater Children’s Hospital, South Brisbane, QLD, 4101, Australia

    Sadasivam Suresh, David M. Cooper & Carolyn Dakin

Authors
  1. Philip I. Terrill
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  2. Stephen J. Wilson
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  3. Sadasivam Suresh
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  4. David M. Cooper
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  5. Carolyn Dakin
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Correspondence to Philip I. Terrill.

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Terrill, P.I., Wilson, S.J., Suresh, S. et al. Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data. Med Biol Eng Comput 50, 851–865 (2012). https://doi.org/10.1007/s11517-012-0918-4

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  • DOI: https://doi.org/10.1007/s11517-012-0918-4

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