Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach
Bibliographic record
Abstract
Globally, heart disease has been the leading cause of death for more than two decades. There is a need to develop intelligent architectures to handle a variety of real life clinical scenarios when a 12-lead ECG is not a viable option. We propose a method using wide and deep CNN architectures to classify cardiac abnormalities from 12, 6, 4, 3, and 2 leads ECGs. These five networks were created for the PhysioNet/CinC Challenge 2021, by the Biomedic2ai team. ECG signals were down-sampled to 100Hz and partitioned with 5-second windows using a sliding 4-second overlap. A one-dimensional deep CNN (1D-dCNN) module was used to preserve sequentially related features embedded in the signals. A feature extraction module was added to the 1D-dCNN, creating a ‘wide and deep modular network’. This framework allows the addition or removal of modules to optimize classification models. We achieved test scores of 0.36, 0.30, 0.31, 0.29, and 0.34 (ranked 23rd, 26th, 26th, 27th, and 22nd out of 39 officially ranked teams) for 12,6,4,3, and 2 leads, respectively, on the hidden test set provided by the challenge. Our model demonstrates potential with the wide modular network. The framework also provides the flexibility to integrate clinical knowledge in the future modules to improve the overall classification performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".