Research Article
Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features
Table 2
The p values between the proposed feature extraction method and each of the existing feature extraction methods.
| Parameter | Classifier | FS1 | FS2 | FS3 | FS4 |
| Accuracy | LDA | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | SVM | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | KNN | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 |
| Sensitivity | LDA | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | SVM | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | KNN | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 |
| Specificity | LDA | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | SVM | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | KNN | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 |
| Precision | LDA | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | SVM | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | KNN | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 |
| F1 score | LDA | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | SVM | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 | KNN | p < 0.001 | p < 0.001 | p < 0.001 | p < 0.001 |
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