Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants
Bibliographic record
Abstract
Introduction: Unmanaged pain in preterm infants can lead to long-term developmental consequences. Current pain assessment methods lack specificity, resulting in possible pain mismanagement in Neonatal Intensive Care Units (NICUs). This study explores the application of machine learning (ML) to differentiate between pain-related and non-pain-related cortical activity in preterm infants. Objective: To evaluate the performance of ML models in distinguishing cortical EEG activity during a painful procedure in preterm infants across different postmenstrual ages (PMAs). Methods: This observational study was conducted from June 2015 to May 2024 at Mount Sinai Hospital in Toronto, Canada, and University College London Hospital, United Kingdom. EEG data were collected from 72 preterm infants (27 females) during routine heel lance procedures while held in skin-to-skin contact. Infants' gestational ages ranged from 24 to 36 weeks with a mean PMA of 32.87 weeks. Five ML models-XGBoost, support vector machines, Random Forest, Logistic Regression (LR), and convolutional neural networks-distinguished EEG activity pre-heel and post-heel lance. Results: Model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUC). In the oldest PMA group (≥34 weeks), LR achieved the highest mean accuracy (82%) and AUC (0.90). Similarly, LR achieved the highest mean accuracy (70%) and AUC (0.94) in the middle PMA group (32-33 weeks, 6 days). In the youngest group (<32 weeks), all models except XGBoost performed relatively the same with a mean accuracy of 76% or 77% and a mean AUC of 0.82 or 0.80. Conclusion: Machine learning models demonstrate potential in distinguishing pain-related cortical activity, offering a pathway for improved neonatal pain assessment in NICUs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".