Analysis of Compressively Sensed Electrocardiogram for Detection of Atrial Fibrillation
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are one of the leading causes of death in the world. Long-term ambulatory electrocardiogram (ECG) monitoring enables timely medical interventions. Effective and efficient ambulatory ECG monitoring can be supported by compressive sensing, which can reduce battery consumption; however, the reconstruction of compressively sensed ECG could be a computationally intensive operation. Therefore, detection of CVDs in compressively sensed ECG is highly desirable. This thesis proposes a three-stage system based on machine learning for detecting atrial fibrillation (AF) in compressively sensed ECG while reducing false alarms (i.e., false positives) due to contamination and reducing reconstruction of the ECG. Stage 1 uses a novel signal quality index (SQI) to represent the quality of the compressively sensed ECG on a continuous scale and reject low-quality ECG. Stage 2 classifies the ECG as either AF or normal and generates an associated confidence score. Stage 3 selectively reconstructs the ECG when the Stage 2 confidence score is unacceptable and classifies the reconstructed ECG as either AF or normal. Clean ECG from Long-Term AF Database was artificially corrupted with simulated motion artifact to pre-set signal-to-noise ratios. The corrupted ECG was compressively sensed at 50% and 75% compression ratios (CRs). The system was evaluated using average precision (AP), the area under the curve (AUC) of the receiver operator characteristic curve, false-positive rate (FPR), and true positive rate (TPR). The system was optimized to maximize the AP and minimize ECG rejection and reconstruction ratios. The optimized system for 50% CR had 0.72 AP, 0.63 AUC, 0.38 rejection ratio, and 0.38 reconstruction ratio. The optimized system for 75% CR had 0.72 AP, 0.63 AUC, 0.40 rejection ratio, and 0.35 reconstruction ratio. The optimized system reduced FPR by 0.37 for both CRs compared to Stage 2 alone. At a fixed FPR of 0.10, the TPR of the proposed system was 0.43 and 0.42 for 50% and 75% CRs, respectively, an improvement of 0.10 and 0.14 over Stage 2. The proposed system reduced the probability of false alarms (FPR) and detected AF in compressively sensed ECG (improved TPR) while being computationally efficient (low reconstruction ratio).
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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) | 0.001 | 0.002 |
| 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) | 0.000 | 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".