Classification on MIT-BIH Arrhythmia Dataset
99.54AccuracyMambaCapsule
Evaluation results
| Method | Links | ||
|---|---|---|---|
| MambaCapsuleArchitecture=Mamba + Capsule Networks2024.07 | 99.54 | 93.5 | |
| El-Ghaish and EldeleArchitecture=MSC + CRM + BiTrans + CAL2024.07 | 99.35 | 94.26 | |
| Nurmaini et al.Architecture=DAE + DNN2024.07 | 99.34 | 91.44 | |
| Kim et al.Architecture=ResNet+ SE block + biLSTM2024.07 | 99.2 | 91.69 | |
| ATDCNN+LSTM Parameters (Millions)=70, Transformer+ViT Parameters (Millions)=2822023.06 | 98.9 | 98.2 | |
| Hammad et al.Architecture=ResNet + LSTM + GA2024.07 | 98 | 89.7 | |
| Xia et al.Architecture=CNN + DAE + Transformer2024.07 | 97.66 | — | |
| Pokaprakarn et al.Architecture=Seq2Seq + CRNN2024.07 | 97.6 | 89 | |
| MSMFCNN+LSTM Parameters (Millions)=88, Transformer+ViT Parameters (Millions)=3052023.06 | 97.5 | 96.8 | |
| EfficientNetWork=EfficientECG2025.12 | 96.5 | 96 | |
| ISRCPCNN+LSTM Parameters (Millions)=95, Transformer+ViT Parameters (Millions)=3102023.06 | 96.3 | 95.1 | |
| BIMCNN+LSTM Parameters (Millions)=82, Transformer+ViT Parameters (Millions)=2982023.06 | 95.8 | 94.3 | |
| Sellami and HwangArchitecture=CNN2024.07 | 95.33 | 80.08 | |
| DWT+random forestWork=Li et al. [18]2025.12 | 94.6 | 81 | |
| Cross-attentionCNN+LSTM Parameters (Millions)=160, Transformer+ViT Parameters (Millions)=3702023.06 | 94.2 | 93.1 | |
| DWT+SVMWork=Martis et al. [20]2025.12 | 93.8 | 78 | |
| Augmentation+CNNWork=Acharya et al. [1]2025.12 | 93.5 | 83 | |
| ResNetWork=Kachuee et al. [15]2025.12 | 93.4 | 87 | |
| LMFCNN+LSTM Parameters (Millions)=71, Transformer+ViT Parameters (Millions)=2832023.06 | 91.2 | 90.5 | |
| Jin et al.Architecture=DLA + CLSTM2024.07 | 88.76 | 80.54 | |
| Unimodal (Numerical/Time-series)2023.06 | 85.8 | 89.3 | |
| Unimodal (Image)2023.06 | 51.8 | 47.9 |