Fetal ECG Extraction Using Input-Mode and Output-Mode Adaptive Filters With Blind Source Separation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents two new approaches of fetal electrocardiogram (ECG) signal (FECG) separation using the input-mode adaptive filter (IMAF) and the output-mode adaptive filter (OMAF). Both approaches use the recursive least-squares (RLS) and the least-mean-squares (LMS) algorithms and a single-reference-generation block. In the IMAF, the filter’s primary input is connected directly to the abdominal signal. The reference signal is generated by windowing the abdominal signal according to the locations of the QRS MECG pulses. In the OMAF, the filter’s primary input is connected to the output stage of a blind source separation block. The reference signal is generated by windowing the raw FECG signal, from the BSS output, according to the locations of the QRS pulses of the extracted MECG signal. We selected the null space idempotent transformation matrix (NSITM) as the BSS algorithm used in this work. Results from real Daisy and Physionet databases show the successful extraction of the FECG signal. Results from synthesized data from Physionet databases, using OMAF, show considerable improvement in extraction performances over NSITM and IMAF when the fetal-to-maternal signal-to-noise ratio (fmSNR) increases from −30 to 0 dB. This study demonstrated that the OMAF is a feasible algorithm for FECG extraction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it