TY - JOUR
T1 - Forecasting respiratory collapse
T2 - Theory and practice for averting life-threatening infant apneas
AU - Williamson, James R.
AU - Bliss, Daniel
AU - Paydarfar, David
N1 - Funding Information:
J.R.W. and D.W.B. were supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract FA8721-05-C-0002, and D.P. was supported by National Institutes of Health (NIH) Grant R01 GM104987 and the Hansjörg Wyss Institute for Biologically Inspired Engineering at Harvard University . Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.
PY - 2013/11/1
Y1 - 2013/11/1
N2 - 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.
AB - 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.
KW - Infant apnea
KW - Machine learning
KW - Monitoring devices
KW - Respiratory control
KW - Signal processing
KW - Statistical modeling
KW - Stochastic resonance
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U2 - 10.1016/j.resp.2013.05.034
DO - 10.1016/j.resp.2013.05.034
M3 - Review article
C2 - 23735485
AN - SCOPUS:84886305357
SN - 1569-9048
VL - 189
SP - 223
EP - 231
JO - Respiratory Physiology and Neurobiology
JF - Respiratory Physiology and Neurobiology
IS - 2
ER -