Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants
Machine learning models demonstrate potential in distinguishing pain-related cortical activity, offering a pathway for improved neonatal pain assessment in NICUs.
Authors
- Hang Du (ORCID: https://orcid.org/0000-0002-0178-9408)
- Vibhuti S Shah (ORCID: https://orcid.org/0000-0001-8685-2447)
- Carol Cheng
- Maria Fitzgerald (ORCID: https://orcid.org/0000-0003-4188-0123)
- Sara Jasim
- Lojain Hamwi (ORCID: https://orcid.org/0000-0002-5591-7583)
- Ian Stedman (ORCID: https://orcid.org/0000-0002-7121-8184)
- Nicole Racine (ORCID: https://orcid.org/0000-0002-6371-6570)
- Lorenzo Fabrizi (ORCID: https://orcid.org/0000-0002-9582-0727)
- Rebecca Pillai Riddell (ORCID: https://orcid.org/0000-0003-3990-3680)
- Xiaogang Wang (ORCID: https://orcid.org/0000-0002-7929-5889)
- Judith Meek (ORCID: https://orcid.org/0009-0006-7443-3046)
Institutions
- Michigan Technological University (US)
- Mount Sinai Hospital (CA)
- University of Ottawa (CA)
- York University (CA)
- Sinai Health System (CA)
- University College London (GB)
Publication Details
- Journal
- PAIN Reports
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1097/pr9.0000000000001332
- Primary Topic
- Pediatric Pain Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00