PCG classification on CirC
97.84AccuracyQiVC-Net
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| QiVC-Net2025.11 | 97.84 | 96.89 | 98.14 | 95.6 | |
| 1D-CNN, wavelet stockwell transform (WST)paper_ref=[57]2025.11 | 96.51 | 96.6 | 96.6 | 96.42 | |
| Stacked autoencoder deep neural network (SAE-DNN)paper_ref=[63]2025.11 | 95.43 | 97.92 | 98.32 | 95.75 | |
| Recognizing recurrent neural network (RRNN)paper_ref=[56]2025.11 | 95.2 | 91.6 | 99.1 | — | |
| Zero-order autonomous learning multiple-model (ALMMo-0)paper_ref=[65]2025.11 | 93.04 | 90.82 | 95.26 | — | |
| 1D U-Net-basedpaper_ref=[64]2025.11 | 92.8 | 94.5 | — | — | |
| Support vector machine (SVM)paper_ref=[59]2025.11 | 91.36 | 80.28 | 94.47 | — | |
| Ensemble classifier (SVM, KNN, DT, ANN, LSTM)paper_ref=[61]2025.11 | 91.23 | 78.81 | 97.04 | — | |
| Hidden semi-Markov model (HSMM), CNNpaper_ref=[58]2025.11 | 86.8 | 87 | 86.6 | — | |
| CNN-LSTMpaper_ref=[60]2025.11 | 86 | 87 | 89 | 91 | |
| Feed-forward neural network (FNN)paper_ref=[62]2025.11 | 85.65 | 86.73 | 84.75 | 84.58 |