Table 1.
Performance comparison between different BP estimation models.
| Model | SBP(mmHg) | DBP(mmHg) | ||
|---|---|---|---|---|
| MAE | RMSE | MAE | RMSE | |
| Support Vector Regression (SVR) [37] | 8.54 | 10.9 | 4.34 | 5.8 |
| Generalized Deep Neural Network (GDNN) [22] | 3.21 | 4.63 | 2.23 | 3.21 |
| Enhanced regression model (ERM) [8] | 4.24 | 5.06 | 4.81 | 6.37 |
| Long Short-Term Memory (LSTM) [11] | 4.05 | 5.25 | 2.41 | 3.17 |
| End-To-End Deep Learning Architecture(ETE) [38] | 4.06 | 5.42 | 3.33 | 4.30 |
| Convolutional neural network (CNN) [39] | 3.68 | 5.75 | 1.97 | 3.52 |
| Our model | 3.96 | 5.54 | 2.39 | 3.45 |