MEJORAMIENTO DE ALGORITMO CLÁSICO DE DETECCIÓN DE COMPLEJOS QRS EN SEÑAL ELECTROCARDIOGRÁFICA
Bibliographic record
Abstract
La identificación temporal de complejos QRS en una señal electrocardiográfica (señal ECG) es una tarea de amplia investigación y numerosas aplicaciones prácticas.Este trabajo presenta las mejoras realizadas a un algoritmo para la detección de complejos QRS de una señal ECG conocido como algoritmo de Holsinger, utilizando características presentes en un detector de complejos QRS conocido como algoritmo de Hamilton-Tompkins.Se detalla cada una de las mejoras graduales realizadas en algoritmo de Holsinger, con la revisión de su rendimiento en la identificación temporal de complejos QRS sobre registros de señales electrocardiográficas (señales ECG) de una base de datos pública (base de datos de arritmias del MIT-MIT-BIH), con el objetivo de demostrar empíricamente que es posible obtener un mejor rendimiento en algoritmo simple de detección de complejos QRS, con la conservación de su simplicidad, mediante la inclusión de técnicas clásicas de procesamiento digital de señales (DSP).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".