STATISTICAL MODELLING OF HEART RATE VARIATIONS IN PRETERM INFANTS TO REDUCE FALSE ALARM RATES
Bibliographic record
Abstract
Objective To develop a model of heart rate variations in preterm infants for detecting false alarms. Methods Infants 150 bpm) and a normal state 100–150 bpm. The probabilities of these infants remaining in each state and changing from states were calculated as a Markov chain probability model using MATLAB 7.5 (Mathworks, Natick, Massachusetts, USA). The Kolmogorov entropy principle (the probability of error increasing the longer it is from an event) was used to calculate the false alarm rate. The probability of the infant’s heart rate reaching alarm states (A1, A2) was computed. These probabilities were compared with the actual changes in the heart rate after 2, 4, 6 and 8 minutes have elapsed from the initial time. Results 45 infants with mean (SD) gestational age of 28.7 (2) weeks, mean (SD) birthweight of 1250 (346) g were studied. The average probabilities of error for predicting state A1 and state A2 are summarised in the table. Conclusions It is feasible to utilise statistical techniques for calculating the probabilities of false alarm rate of heart rate signals in preterm infants using statistical techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".