Detection of Abnormal Heartbeats in Compressed Electrocardiograms
Bibliographic record
Abstract
The electrocardiogram (ECG) is an important measurement for diagnosing heart disease. Transmission of continuous ECG over a wireless network can be taxing; therefore, compressing the ECG can reduce the load on wireless networks. On the other hand, reconstructing the ECG for analysis can be computationally intensive. As such, diagnosing heart diseases from compressed ECG is desired. Abnormal beat detection using machine learning in the compressed domain is proposed. The ECG was compressed using a wavelet-based morphological feature preserving compression algorithm The compression algorithm was applied on 84 ECG records available in Long Term Atrial Fibrillation Database (LTAFDB) achieving an average compression rate of 4.17:1. Abnormal beats in the compressed signals were classified using a Random Forest trained using a randomly under-sampled training set. The achieved true positive rate was 69.5% and the false positive rate was 32.8%. The results indicate that identification of abnormal beats in compressed ECGs is possible. Future work will explore detection of abnormal beats in compressively sensed ECG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".