Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data
Bibliographic record
Abstract
In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing conditions such as left bundle branch block. To address the limited availability of ECG databases with QRS delineation labels, we present a method to use small databases to train deep learning object detection models for global QRSd estimation that involves minimal manual labeling of median beats. In our method, an ECG record is segmented into individual heartbeats, transformed into artificial images, and a Faster R-CNN model is utilized to estimate the global QRSd. Faster R-CNN models were tested with three different backbone configurations (VGG-16, VGG-19, and RESNET-18) and two ECG image formats: binary images in which each beat in each lead was represented by a separate image and RGB images in which the same beat from a trio of leads was superimposed by mapping each lead to a different color channel. Using 258 twelve-lead, 10-s digital ECG records acquired from 140 unique heart failure outpatients, the best-performing backbone, VGG-19 with RGB images, achieved root-mean-square and mean absolute errors for QRSd of 10.4 ± 0.8 ms and 8.2 ± 1.0 ms, respectively, during five-fold cross-validation. Testing with an independent, publicly available dataset yielded root-mean-square and mean absolute errors for QRSd of 7.0 ± 1.1 ms and 5.3 ± 0.9 ms, respectively. Therefore, our method provides high QRSd estimation accuracy while reducing the need for manual labeling and shows promise for generalization to independent databases, demonstrating potential for efficient training of deep learning models on small ECG databases. • A Faster R-CNN model estimates global QRSd using artificial ECG images. • VGG-19 with RGB images achieved the best QRSd estimation accuracy. • The method reduces manual labeling by using small ECG databases for training. • Strong performance was observed in both cross-validation and independent testing. • The approach shows potential for generalizing to new ECG datasets.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".