Multi-Stage Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram
Bibliographic record
Abstract
Atrial Fibrillation (AF) is a cardiac condition that can be asymptomatic and can lead to increase risk of stroke, heart attack, or death. Long term monitoring of ECG is typically used to diagnose AF. However, long term monitoring of ECG generates a large amount of data that can increase power consumption, storage requirements, and wireless transmission bandwidth. Compressive Sensing (CS) is a compression technique that reduces the amount of data collected and the power consumption of ECG recording devices. However, reconstruction of compressively sensed ECG is a computationally expensive technique. This paper proposes a two-stage AF detection system that detects AF in the compressed domain and only reconstructs ECG segments with low detection confidence to confirm the detection of AF. The system was tested using the Long-Term Atrial Fibrillation Database (LTAFDB) available on Physionet. The system is based on Random Forest built using features extracted using discrete cosine transform, statistical methods, empirical mode decomposition, and wavelet transform. The system achieved an area under the curve (AUC) of receiver operator curve of 0.95 at 50% and 75% compression. The weighted average precision (AP) was 0.94 at 50% and 75% compression, and the F1 score was 0.90 and 0.91 at 50% and 75% compression, respectively. The system was tested using 10-fold record-based cross-validation. Confirming AF detection by reconstructing ECG where AF was detected with low confidence has improved AP, AUC, and F1 score over using an AF detector in the compressed domain only while judicially increasing usage of computational resources.
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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".