Papers › Comparing feature-based classifiers and convolutional neural networks to detect...
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG
Fernando Andreotti, Oliver Carr, Marco A. F. Pimentel, Adam Mahdi, Maarten De Vos
The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet. The feature-based classifier obtained an F1 score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Arrhythmia Detection | The PhysioNet Computing in Cardiology Challenge 2017 | ResNet (16 CF, 60s SEG) | Accuracy (TEST-DB) | 79% | #2 of 5 | Archive leaderboard | report |
| Arrhythmia Detection | The PhysioNet Computing in Cardiology Challenge 2017 | ResNet (16 CF, 60s SEG) | Accuracy (TRAIN-DB) | 62.4% | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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