T wave alternans evaluation using adaptive time–frequency signal analysis and non-negative matrix factorization
Bibliographic record
Abstract
Each year 400,000 North Americans die from sudden cardiac death (SCD). Identifying those patients at risk of SCD remains a formidable challenge. T wave alternans (TWA) evaluation is emerging as an important tool to risk stratify patients with heart diseases. TWA is a heart rate dependent phenomenon that manifests on the surface electrocardiogram (ECG) as a change in the shape or amplitude of the T wave every second heart beat. The presence of large magnitude TWA often presages lethal ventricular arrhythmias. Because the TWA signal is typically in the microvolt range, accurate detection algorithms are required to control for confounding noise and changing physiological conditions (i.e. data nonstationarity). In this study, we address the limitations of two common TWA estimation methods, spectral method (SM) and modified moving average (MMA). To overcome their limitations, we propose a modified TWA quantification framework, called Adaptive SM, that uses non-linear time-frequency distribution (TFD). In order to increase the robustness of TWA detection in ambulatory ECGs, we also propose a new technique, called non-negative matrix factorization (NMF)-Adaptive SM. We present the analytical background of these methods, and evaluate their accuracy in detecting synthetic TWA signal in simulated and real-world ambulatory ECG recordings under conditions of noise and data non-stationarity. The results of the numerical simulations support the effectiveness of the proposed approaches for TWA analysis, which may ultimately improve SCD risk assessment.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".