Forecasting respiratory collapse: Theory and practice for averting life-threatening infant apneas

James R. Williamson, Daniel Bliss, David Paydarfar

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Apnea of prematurity is a common disorder of respiratory control among preterm infants, with potentially serious adverse consequences on infant development. We review the capability for automatically assessing apnea risk and predicting apnea episodes from multimodal physiological measurements, and for using this knowledge to provide timely therapeutic intervention. We also review other, similar clinical domains of respiratory distress assessment and prediction in the hope of gaining useful insights. We propose an algorithmic framework for constructing discriminative feature vectors from physiological measurements, and for building robust and effective statistical models for apnea assessment and prediction.

Original languageEnglish (US)
Pages (from-to)223-231
Number of pages9
JournalRespiratory Physiology and Neurobiology
Volume189
Issue number2
DOIs
StatePublished - Nov 1 2013

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Apnea
Hope
Statistical Models
Child Development
Premature Infants
Therapeutics

Keywords

  • Infant apnea
  • Machine learning
  • Monitoring devices
  • Respiratory control
  • Signal processing
  • Statistical modeling
  • Stochastic resonance

ASJC Scopus subject areas

  • Physiology
  • Pulmonary and Respiratory Medicine
  • Neuroscience(all)

Cite this

Forecasting respiratory collapse : Theory and practice for averting life-threatening infant apneas. / Williamson, James R.; Bliss, Daniel; Paydarfar, David.

In: Respiratory Physiology and Neurobiology, Vol. 189, No. 2, 01.11.2013, p. 223-231.

Research output: Contribution to journalArticle

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