Noise reduction by perfect-translation-invariant complex discrete wavelet transforms for fetal electrocardiography and magnetocardiography
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Bibliographic record
Abstract
Echocardiography is widely used for the diagnosis of fetal cardiac arrhythmias. However, this method does not detect configurational changes in the electrocardiogram (ECG) such as life-threatening changes in QRS and the prolongation of the QT interval. Fetal magnetocardiography (fMCG) and fetal electrocardiography (fECG) are valuable tools for the detection of electrophysiological cardiac signals although both have certain limitations. Such techniques must deal with excess internal noise such as maternal respiratory movements, fetal movements, muscle contraction and fetal body movement and external noise (e.g., electromagnetic waves). Heart rate variability (HRV) is a well-known phenomenon with fluctuation in the time interval between heartbeats. The lack of translation invariance is a serious defect in the conventional wavelet transforms (discrete wavelet transform (DWT)). Fluctuation of the impulse response at each energy level is observed in the multi-resolution analysis (MRA). Configurational changes in the ECG waveforms are frequently observed after noise reduction by the conventional wavelet transforms. Both the lack of translation invariance of conventional wavelet transforms and HRV cause deformation of the ECG waveforms. We describe here the CDWTs with perfect translation invariance (PTI). Compared with conventional wavelets, PTI of the fECG and fMCG resulted in only minor configurational changes in the ECG waveforms. This technique yields persistently stable ECG waveforms, including P wave and QRS complex. First, an independent component analysis (ICA) was applied to fECG or fMCG data to remove noise. We provide an example to show that the morphological change in QRS complex is barely affected when PTI is applied to normal fECG. Examples of fetal arrhythmias, such as ventricular trigeminy, ventricular bigeminy and premature atrial contraction are demonstrated using this technique. The results lead us to the conclusion that ICA and noise reduction in fECG and fMCG by PTI are promising methods for the diagnosis of fetal arrhythmia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| 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