Arrhythmia Detection On The Physionet
Metrics
F1 (Hidden Test Set)
Results
Performance results of various models on this benchmark
Model Name | F1 (Hidden Test Set) | 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 |
0 of 5 row(s) selected.