Real-time fetal ECG extraction from multichannel abdominal ECG using compressive sensing and ICA
Bibliographic record
Abstract
An improved method for separation of fetal electrocardiogram (fECG) from abdominal electrocardiogram (abdECG) is proposed in this paper. Proposed method combines two widely used techniques i.e. compressive sensing (CS) and independent component analysis (ICA). Separation of fECG is carried out by applying ICA directly on the compressed signal. The efficient improved ℓp-regularized least-sqaures (ℓp-RLS) algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on Physionet datasets which contain 75 records in set-A, 100 records in set-B and 6 records in Silesia dataset. The obtained outcomes reveal that proposed algorithm shows promising results (Sensitivity S=92%, Positive predictivity P+ = 93%, F1 measure = 92.5% with average percentage root-mean-square difference PRD =6.89% and Execution time= 2.91 sec.). The results also indicate that there is a substantial improvement in quality of reconstructed signal which is achieved by maintaining lowest PRD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".