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Table 5 Classification precision, recall and accuracy calculated by selected classifiers at different EEG data bands when duration (k) is considered as the discriminative (or input) feature for classification

From: EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

 

Alpha

Beta

Delta

Theta

Accuracy, Precision, Recall

 Random Forest

0.552, 0.558, 0.552

0.694, 0.757, 0.694

0.506, 0.5064, 0.506

0.530, 0.539, 0.530

 SVM (Linear)

0.430, 0.2539, 0.430

0.522, 0.431, 0.522

0.478, 0.336, 0.478

0.512, 0.432, 0.512

 SVM (RBF)

0.498, 0.429, 0.498

0.502, 0.291, 0.502

0.514, 0.440, 0.514

0.498, 0.249, 0.498

 Decision Tree

0.568, 0.5721, 0.568

0.730, 0.752, 0.730

0.544, 0.546, 0.544

0.604, 0.613, 0.604

 kNN

0.576, 0.5838, 0.576

0.808, 0.839, 0.808

0.560, 0.584, 0.560

0.660, 0.668, 0.660

 Gradient Boost

0.580, 0.587, 0.580

0.718, 0.751, 0.718

0.612, 0.618, 0.612

0.640, 0.653, 0.640