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Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol
Jean-Michel Roué1, Iris Morag2, Wassim M Haddad3
1Neonatal & Pediatric Intensive Care Unit, Brest University Hospital, University of Western Brittany, Brest, France jean-michel.roue@chu-brest.fr.
BMJ Open
|January 7, 2021
Summary
Accurately assessing infant pain is challenging. This study explores a new multimodal approach using sensors and machine learning for objective, continuous pain monitoring in newborns and infants.
Area of Science:
- Biomedical Engineering
- Neonatal Medicine
- Pain Management
Background:
- Objective pain assessment in non-verbal infants is difficult, relying on subjective measures.
- Pain in newborns can lead to long-term behavioral and cognitive issues.
- Current pain assessments are labor-intensive and observer-dependent.
Purpose of the Study:
- To develop and evaluate a multimodal pain assessment approach for non-verbal infants.
- To enable objective, patient-centered, and context-dependent pain measurement.
- To potentially allow for continuous pain monitoring in clinical settings.
Main Methods:
- Utilizing facial electromyography, ECG, electrodermal activity, oxygen saturation, and electroencephalography.
- Employing sensor-fusion and machine-learning algorithms for data analysis.
- Conducting a prospective observational study in 60 preterm and term newborns/infants.
Main Results:
- The multimodal approach has the potential to improve pain assessment accuracy.
- This method may offer continuous pain monitoring capabilities.
- Feasibility will be assessed in a study of infants up to 6 months old.
Conclusions:
- A multimodal, sensor-based approach offers a promising avenue for objective infant pain assessment.
- Further validation and refinement are planned to optimize sensor requirements.
- This technology could significantly advance neonatal and infant pain management.
