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Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state.
Zhang, Dandan; Peng, Zheng; Sun, Shaoxiong; van Pul, Carola; Shan, Caifeng; Dudink, Jeroen; Andriessen, Peter; Aarts, Ronald M; Long, Xi.
Afiliación
  • Zhang D; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Peng Z; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Sun S; Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • van Pul C; Department of Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands.
  • Shan C; Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom.
  • Dudink J; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Andriessen P; Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Aarts RM; Department of Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands.
  • Long X; College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China.
Acta Paediatr ; 113(6): 1236-1245, 2024 06.
Article en En | MEDLINE | ID: mdl-38501583
ABSTRACT

AIM:

This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors.

METHODS:

We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient.

RESULTS:

A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11).

CONCLUSION:

Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sueño / Recien Nacido Prematuro Límite: Female / Humans / Male / Newborn Idioma: En Revista: Acta Paediatr Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Noruega
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sueño / Recien Nacido Prematuro Límite: Female / Humans / Male / Newborn Idioma: En Revista: Acta Paediatr Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Noruega