DeepFilter: an ECG baseline wander removal filter using deep learning\n techniques
Bibliographic record
Abstract
According to the World Health Organization, around 36% of the annual deaths\nare associated with cardiovascular diseases and 90% of heart attacks are\npreventable. Electrocardiogram signal analysis in ambulatory\nelectrocardiography, during an exercise stress test, and in resting conditions\nallows cardiovascular disease diagnosis. However, during the acquisition, there\nis a variety of noises that may damage the signal quality thereby compromising\ntheir diagnostic potential. The baseline wander is one of the most undesirable\nnoises. In this work, we propose a novel algorithm for BLW noise filtering\nusing deep learning techniques. The model performance was validated using the\nQT Database and the MIT-BIH Noise Stress Test Database from Physionet. In\naddition, several comparative experiments were performed against\nstate-of-the-art methods using traditional filtering procedures as well as deep\nlearning techniques. The proposed approach yields the best results on four\nsimilarity metrics: the sum of squared distance, maximum absolute square,\npercentage of root distance, and cosine similarity with 4.29 (6.35) au, 0.34\n(0.25) au, 45.35 (29.69) au and, 91.46 (8.61) au, respectively. The source code\nof this work, containing our method and related implementations, is freely\navailable on Github.\n
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".