Bayesian non-Gaussian autoregressive model with outliers for cardiac signal
Bibliographic record
Abstract
In this paper, we present a novel model for analyzing electrocardiogram (ECG) time series data. The particular focus here is on the RR interval which is the time elapsed between two successive R waves of the QRS signal on the electrocardiogram. Here, we develop a formal statistical approach to overlapping segments (about 25 minutes) of publicly available RR interval time series data using a novel approach that is based on a non-Gaussian autoregressive model, Bayesian inference and includes latent variables to identify outliers. A suitable MCMC sampler was applied and validated in a simulation study. The estimation included a large set of 7 753 overlapping segments for 87 participants which include controls, patients diagnosed with atrial fibrillation and patients diagnosed with congestive heart failure. The simulation studies and analysis of the ECG signal demonstrates that the proposed approach (in particular, the AR structure with latent variables) is flexible in modeling the complex structure of RR segments, while providing the additional benefit of being able to filter out outliers. Based on the conclusions drawn for overlapping segments, it is possible to extend the model in many directions, including application to other specific RR data, longer segments or filtering outliers in other ways.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".