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Atrial Fibrillation Detection On Mit Bih Af
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| ECGNET | 99.40% | ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention | - |
| Wavelet transform + 2D CNN | 99.16% | Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks | - |
| Symbolic dynamics and Shannon entropy | 97.57% | Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy | - |
| CNN-BLSTM | 96.59% | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals | - |
| /spl Delta/RR intervals | 94.95% | A method for detection of atrial fibrillation using RR intervals | - |
| Spectrogram + ConvNet | 93.16% | Atrial Fibrillation Detection Using Deep Features and Convolutional Networks | - |
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