Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram Measurements
Bibliographic record
Abstract
Atrial fibrillation (AF) is a serious cardiovascular condition that can lead to complications, including but not limited to stroke, heart attack, and death. AF can be diagnosed using an electrocardiogram (ECG); however, continuous monitoring produces a large amount of data that can increase storage, power, and transmission bandwidth requirements. Compressive sensing has been used to mitigate increased requirements of continuous monitoring. An AF detector using a deterministic compressively sensed ECG is proposed. By detecting AF in the compressed domain, the computationally expensive process of reconstructing the ECG can be avoided. The detector was based on a random forest trained on features extracted using the wavelet transform, empirical mode decomposition, discrete cosine transform, and statistical methods. ECG data from the long-term AF Database available on PhysioNet were used. The performances of the detectors trained using features from compressed and uncompressed ECG were compared. Using the trained detector, the area under the receiver operating curve (AUC) and the weighted average precision (AP) were both 0.93 for uncompressed data using record-based tenfold cross validation. The AUC and AP were 0.91 and 0.90 at 50% compression, 0.92 and 0.91 at 75% compression, and 0.82 and 0.91 at 95% compression, respectively.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".