Effect of Noise on T‐Wave Alternans Measurement in Ambulatory ECGs Using Modified Moving Average versus Spectral Method
Bibliographic record
Abstract
BACKGROUND: The modified moving average (MMA) and spectral method (SM) are commonly used to measure T-wave alternans (TWA), but their accuracy has not been compared in ambulatory electrocardiograms (ECGs) where TWA signal-to-noise ratio is low. Our objective was to compare the effect of noise and signal nonstationarity on the accuracy of TWA measurement using MMA versus SM when applied to synthetic and ambulatory ECGs. METHODS: Periodic and nonperiodic noise were added to noiseless synthetic ECGs. Simulated TWA (0-20 microV) was added to synthetic ECGs and ambulatory ECG recordings. TWA was measured using SM and MMA, and the measurement error relative to added TWA was compared. An MMA ratio was used to discriminate TWA signal from noise. Signal nonstationarity was simulated by changing heart rate, TWA magnitude, and TWA phase. RESULTS: With no added TWA, MMA falsely measured TWA in synthetic and ambulatory ECGs, while false measurement was not seen with SM. An MMA ratio > 1.2 eliminated false TWA detection. In the presence of low TWA magnitude (<10 microV), TWA was overestimated by MMA and underestimated by SM in proportion to the noise level. In synthetic ECGs with periodic noise and 10-microV added TWA, MMA was less accurate than SM. The effects of simulated signal nonstationarity on the TWA magnitude measured with MMA versus SM were similar using a 64-beat analysis window. CONCLUSIONS: In the presence of noise, MMA falsely detects or overestimates simulated TWA in ambulatory ECG recordings. In this setting, the proposed MMA ratio improves the specificity of MMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".