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

Respiratory Physiology and Neurobiology

Volumn 189, Issue 2, 2013, Pages 223-231

  Williamson, James R ^a   Bliss, Daniel W ^b   Paydarfar, David ^c,d  

^a MIT LINCOLN LABORATORY   (United States)

^b Arizona State University   (United States)

^c UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

^d HARVARD UNIVERSITY   (United States)

Author keywords

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

Indexed keywords

CAFFEINE;

APPARENT LIFE THREATENING EVENT; HEART RATE VARIABILITY; HUMAN; MEASUREMENT; NEWBORN; NEWBORN APNEA; NEWBORN MONITORING; NONREM SLEEP; POSITIVE END EXPIRATORY PRESSURE; PREDICTION; PREMATURITY; PRIORITY JOURNAL; RESPIRATION CONTROL; RESPIRATORY DISTRESS; REVIEW; RISK ASSESSMENT; VENTILATED PATIENT;

INFANT APNEA; MACHINE LEARNING; MONITORING DEVICES; RESPIRATORY CONTROL; SIGNAL PROCESSING; STATISTICAL MODELING; STOCHASTIC RESONANCE;

APNEA; FORECASTING; HUMANS; INFANT, NEWBORN; INFANT, PREMATURE; INFANT, PREMATURE, DISEASES; PREDICTIVE VALUE OF TESTS; PULMONARY ATELECTASIS; RESPIRATION, ARTIFICIAL;

EID: 84886305357     PISSN: 15699048     EISSN: 18781519     Source Type: Journal    
DOI: 10.1016/j.resp.2013.05.034     Document Type: Review

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