Discriminative sparse coding of ECG during ventricular arrhythmias using LC-K-SVD approach
Bibliographic record
Abstract
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are two major types of ventricular arrhythmias that results due to abnormalities in the electrical activation in the ventricles of the heart. VF is the lethal of the two arrhythmias, which may lead to sudden cardiac death. The treatment options for the two arrhythmias are different. Therefore, detection and characterization of the two arrhythmias is critical to choose appropriate therapy options. Due to the time-varying nature of the signal content during cardiac arrhythmias, modeling and extracting information from them using time and frequency localized functions would be ideal. To this effect, in this work, we perform discriminative sparse coding of the ECG during ventricular arrhythmia with hybrid time-frequency dictionaries using the recently introduced Label consistent K-SVD (LC-K-SVD) approach. Using 944 segments of ventricular arrhythmias extracted from 23 patients in the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases, an overall classification accuracy of 71.55% was attained with a hybrid dictionary of Gabor and symlet4 atoms. In comparison, for the same database and non-trained dictionary (i.e the original dictionary) the classification accuracy was found to be 62.71%. In addition, the modeling error using the trained dictionary from LC-K-SVD approach was found to be significantly lower to the one using the non-trained dictionary.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".