Adaptive artefact canceller filter based on penguins search optimisation algorithm for ECG signals
Why this work is in the frame
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Bibliographic record
Abstract
The electrocardiogram (ECG) signal is a collection of biopotentials related to the contractions of heart muscles that is used to diagnose cardiac abnormalities. The ECG signal is usually distorted by unwanted interference called noise or artefact. The removal of such noise is crucial to better analysis of ECG signals and to better evaluation of the human cardiac system. So, in this paper, an enhanced adaptive artefact canceller (AAC) is presented for filtering the ECG signals. The PeSOA algorithm is used to optimise the weight parameters of AAC. The performance of the proposed PeSOA is evaluated in terms of mean square error (MSE), signal-to-noise ratio (SNR), normalised mean square error (NRMSE), correlation, and coherence factor. Besides, the performance of the proposed scheme is compared with that of different existing filtering techniques, such as bacterial foraging optimisation-based AAC (BFOAAC) and AAC. This proposed noise canceller method supports the human cardiac system for analysing the ECG signals preciously.
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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 | 0.000 | 0.000 |
| 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