Med Biol Eng Comput - Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data.

Tópicos

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Resumo

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.

Resumo Limpo

previous work identifi nonlinear variabl calcul respiratori data vari sleep state variabl deriv nonlinear analyt tool recurr quantif analysi rqa accur infant sleep state discrimin studi aim appli discrimin automat classifi s epoch infant sleep rem nonrem wake polysomnogram obtain healthi infant week month age manual sleep stage wake rem nonrem interbreath interv data extract respiratori induct plethysmograph rqa appli calcul radius determin laminar timeseri statist spectral analysi variabl also calcul nest crossvalid method use identifi optim featur subset train evalu linear discrimin analysisbas classifi rqa featur radius laminar reliabl select mean agreement week month classifi perform better comparison classifi includ rqa variabl perform sleepstag tool compar favour interhuman agreement rate improv upon previous system use respiratori data applic includ diagnost screen populationbas sleep research

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