Table 5.
Comparison of the classification accuracies achieved by different studies using different datasets.
| The study reference | Feature extraction method | Classifier | Classification accuracy (%) |
|---|---|---|---|
| [4] | AR + DWT | ANFIS | 95 |
| [5] | DWT | PSO-SVM | 97.41 |
| [13] | MUSIC | Combined neural network (CNN) | 94 |
| [11] | DWT | FSVM | 97.67 |
| [10] | DWT | Evolutionary SVM | 97 |
| [15] | AR | WNN | 90.7 |
| [21] | DWT | Bagging ensemble with SVM | 99 |
| [34] | Fuzzy k-means | SVM | 86.14. |
| [35] | Peaks of MUAPs. | SVM | 95.90 |
| [36] | Time domain features | Adaptive fuzzy k-NN | 93.5 |
| [39] | CWT | Convolutional neural network | 96.80 |
| Proposed method | WPD | AdaBoost with RF | 99.08 |