Adaptive artefact canceller filter based on penguins search optimisation algorithm for ECG signals
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".