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Arrhythmia Detection On The Physionet
Metrics
F1 (Hidden Test Set)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| ResNet + Expert Features | 0.825 | ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks | - |
| Feature-based approach (no segmentation) | - | An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave | - |
| ResNet (16 CF, 60s SEG) | - | Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG | - |
| Towards Understanding ECG Rhyth | - | Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings | - |
| Feature-based approach (10 s segments) | - | An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave | - |
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