Bibliographic record
Abstract
Atrial Fibrillation (AF) is the most common pathologic of sinus tachycardia, which is the result of an increased rate of depolarization in the sinoatrial node (the sinoatrial node discharges electrical impulses at a higher frequency than normal). In this light, its detection at an early stage is essential for treatments prescription. In this context, we propose an artificial neural network (ANN) architecture using ECG patterns to perform the AF detection. ECG signals are classified into three classes (Normal Sinus Rhythm, abnormal signal with AF, and noisy ECG signals). The proposed technique has been implemented on two types of databases (MIT-BIH database and processed MIT-BIH) using two different experiments. Data segments of 10 seconds length have been used. The achieved experimental results proved that the proposed ANN technique has excellent accuracy results without the need for feature extraction to reduce information parameters. Our work has surpassed the state of the art in terms of specificity, precision, and accuracy. Therefore, we enable clinicians to detect automatically the patients with AF disease.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".