MEJORAMIENTO DE ALGORITMO CLÁSICO DE DETECCIÓN DE COMPLEJOS QRS EN SEÑAL ELECTROCARDIOGRÁFICA
Bibliographic record
Abstract
La identificacin temporal de complejos QRS en una seal electrocardiogrfica (seal ECG) es una tarea de amplia investigacin y numerosas aplicaciones prcticas. Este trabajo presenta las mejoras realizadas a un algoritmo para la deteccin de complejos QRS de una seal ECG conocido como algoritmo de Holsinger, utilizando caractersticas 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 revisin de su rendimiento en la identificacin temporal de complejos QRS sobre registros de seales electrocardiogrficas (seales ECG) de una base de datos pblica (base de datos de arritmias del MIT-MIT-BIH), con el objetivo de demostrar empricamente que es posible obtener un mejor rendimiento en algoritmo simple de deteccin de complejos QRS, con la conservacin de su simplicidad, mediante la inclusin de tcnicas clsicas de procesamiento digital de seales (DSP).
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".