Table 8 Calculated classification accuracy, precision and recall using all frequency bands (All), and excluding alpha-band (All-alpha), beta-band (All-beta), delta-band (All-delta) and theta-band (All-theta)

Accuracy, Precision, Recall using occurrence (k) as the discriminative (input) feature

 Random Forest

0.696, 0.732, 0.696

0.700, 0.738, 0.700

0.536, 0.537, 0.536

0.708, 0.741, 0.708

0.734, 0.784, 0.734

 SVM (Linear)

0.732, 0.758, 0.732

0.734, 0.768, 0.734

0.396, 0.391, 0.396

0.724, 0.756, 0.724

0.700, 0.725, 0.700

 SVM (RBF)

0.730, 0.774, 0.730

0.724, 0.772, 0.724

0.524, 0.535, 0.524

0.728, 0.772, 0.728

0.758, 0.793, 0.758

 Decision Tree

0.646, 0.683, 0.646

0.680, 0.711, 0.680

0.602, 0.615, 0.602

0.656, 0.682, 0.656

0.680, 0.715, 0.680

 kNN

0.794, 0.814, 0.794

0.822, 0.841, 0.822

0.694, 0.708, 0.694

0.762, 0.788, 0.762

0.802, 0.831, 0.802

 Gradient Boost

0.692, 0.729, 0.692

0.696, 0.746, 0.696

0.654, 0.669, 0.654

0.688, 0.732, 0.688

0.698, 0.731, 0.698

Accuracy, Precision, Recall using duration (k) as the discriminative (input) feature

 Random Forest

0.686, 0.735, 0.686

0.684, 0.741, 0.684

0.538, 0.544, 0.538

0.674, 0.747, 0.674

0.670, 0.731, 0.670

 SVM (Linear)

0.690, 0.740, 0.690

0.740, 0.806, 0.740

0.464, 0.401, 0.464

0.614, 0.668, 0.614

0.662, 0.733, 0.662

 SVM (RBF)

0.614, 0.758, 0.614

0.590, 0.713, 0.590

0.598, 0.623, 0.598

0.694, 0.716, 0.694

0.578, 0.678, 0.578

 Decision Tree

0.662, 0.685, 0.662

0.718, 0.743, 0.718

0.584, 0.587, 0.584

0.730, 0.758, 0.730

0.644, 0.668, 0.644

 kNN

0.724, 0.746, 0.724

0.744, 0.773, 0.744

0.530, 0.541, 0.530

0.776, 0.820, 0.776

0.740, 0.765, 0.740

 Gradient Boost

0.754, 0.796, 0.754

0.772, 0.810, 0.772

0.632, 0.636, 0.632

0.722, 0.767, 0.722

0.710, 0.743, 0.710

Accuracy, Precision, Recall using coverage (k) as the discriminative (input) feature

 Random Forest

0.730, 0.776, 0.730

0.734, 0.782, 0.734

0.560, 0.567, 0.560

0.710, 0.769, 0.710

0.754, 0.794, 0.754

 SVM (Linear)

0.674, 0.702, 0.674

0.664, 0.696, 0.664

0.460, 0.439, 0.460

0.672, 0.707, 0.672

0.672, 0.704, 0.672

 SVM (RBF)

0.618, 0.6631, 0.618

0.620, 0.666, 0.620

0.380, 0.354, 0.380

0.634, 0.681, 0.634

0.616, 0.656, 0.616

 Decision Tree

0.698, 0.711, 0.698

0.738, 0.753, 0.738

0.682, 0.692, 0.682

0.678, 0.704, 0.678

0.628, 0.661, 0.628

 kNN

0.776, 0.806, 0.776

0.764, 0.786, 0.764

0.634, 0.644, 0.634

0.778, 0.802, 0.778

0.738, 0.784, 0.738

 Gradient Boost

0.762, 0.804, 0.762

0.782, 0.815, 0.782

0.696, 0.707, 0.696

0.758, 0.797, 0.758

0.748, 0.792, 0.748

Accuracy, Precision, Recall using combination of occurrence (k), duration (k) and coverage (k) as the discriminative (input) feature

 Random Forest

0.678, 0.738, 0.678

0.698, 0.764, 0.698

0.562, 0.567, 0.562

0.688, 0.757, 0.688

0.710, 0.779, 0.710

 SVM (Linear)

0.712, 0.753, 0.712

0.734, 0.792, 0.734

0.514, 0.488, 0.514

0.612, 0.634, 0.612

0.656, 0.742, 0.656

 SVM (RBF)

0.530, 0.337, 0.530

0.500, 0.250, 0.500

0.494, 0.308, 0.494

0.514, 0.393, 0.514

0.508, 0.268, 0.508

 Decision Tree

0.692, 0.725, 0.692

0.698, 0.719, 0.698

0.612, 0.624, 0.612

0.660, 0.690, 0.660

0.646, 0.678, 0.646

 kNN

0.724, 0.746, 0.724

0.744, 0.773, 0.744

0.530, 0.540, 0.530

0.776, 0.820, 0.776

0.740, 0.7652, 0.740

 Gradient Boost

0.754, 0.792, 0.754

0.772, 0.805, 0.772

0.702, 0.712, 0.702

0.714, 0.763, 0.714

0.758, 0.786, 0.758