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Arrhythmia Detection On Mit Bih Ar
Metrics
Accuracy (Inter-Patient)
Accuracy (Intra-Patient)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| BiRNN | 99.53% | 99.92% | Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach | |
| BiLSTM-Attention | 99.47% | - | Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network | - |
| ESN+Reservoir Computing | 99.11% | - | Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices | - |
| Deep residual CNN | 93.4% | - | ECG Heartbeat Classification: A Deep Transferable Representation | |
| TVCG_PSO | 92.4% | - | Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO | - |
| SVM | 76.3% | 98.7% | Support vector machine based arrhythmia classification using reduced features | - |
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