Electrocardiogram data mining based on frame classification by dynamic time warping matching
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it