Automated Detection of Fetal Movements Based on Baseline Calculation and Fetal Heart Rate Accelerations
Bibliographic record
Abstract
Fetal electronic monitoring is extensive and important in obstetrics. Although fetal movement is ususally used as an important indicator for quantifying fetal wellbeing, non-invasive and long-term monitoring of fetal movement remains challenging. The object of this study is to develop an algorithm for automatic detection of the fetal movements based on the analysis of Doppler ultrasound signals. In order to detect fetal movements automatically, a two-step process was proposed to track fetal movement. In Step 1, to suppress the problem of error detection, we calculated the baseline of the fetal movement signals from actography to extract new signals. In step2, we recalculated the threshold value of fetal movement detection by utilizing the information of fetal heart rate (FHR) acceleration to produced adaptive threshold values. The results showed that the union of results detected by the proposed method from actography and tocography achieved an encouraging performance with highest sensitivity and acceptable positive predictive value (PPV).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".