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1618 Identification of Noxious Events for Newborn Infants with a Neural Network
  1. N Bressan1,
  2. C McGregor1,2,
  3. M Blount3,
  4. M Ebling3,
  5. D Sow3,
  6. A James4,5
  1. 1Business & Information Technology
  2. 2Health Sciences, University of Ontario Institute of Technology, Oshawa, ON, Canada
  3. 3IBM TJ Watson Laboratory, Hawthorne, NJ, USA
  4. 4Paediatrics, The Hospital for Sick Children
  5. 5University of Toronto, Toronto, ON, Canada


Background Recognition of pain experienced by immature and/or critically ill newborn infants in the Neonatal Intensive Care Unit remains a challenge despite the use of objective scoring systems that depend on physiological and behavioural parameters. We consider there is a need to identify pain using only physiological data streams.

Methods Data were collected from three preterm male, gestational age 27.25±0.95 weeks (mean±SD), birth weight 941.25±189.31 grams. Heart rate (HR), mean arterial pressure (MAP), respiratory rate (RR), blood oxygen saturation (SpO2) were considered for the NN Input Vector. NN’s output were set to ‘1’ for noxious stimuli pattern (NSP) define as: HR≥160 AND MAP≥55 AND RR≥40 AND SpO2< < 901. Artifact events were captured in previous study2 and integrated with real-time physiological data streams. In this study we correlated the nociceptive event identified by NN with the artifact nociceptive event.

Results Events ‘vascular access’ and ‘reintubation’ statistically coincide with the NSP defined in 100%. Event ‘routine care’ coincides in 52.67% with NSP. HR≥160 was evident in 24.6% of the event occurrence. The events were successfully identified by the NN, shown figure 1.

Conclusion This study showed correlation of artifact nociceptive event with the physiological data streams NN patterns verifying a positive relation between nociceptive response and non-invasive physiological response. NN developed previously proved to be an accurate tool for deployment in a clinical decision support system.


  1. Submitted to ESCTAIC 2012, Romania.

  2. 1st ACM International Health Informatics Symposium, 647–655.

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