Research Article
Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data
Table 5
Evaluation metrics result obtained on prediction of multiclass problems.
| Classes | Number of features | Classifier | Accuracy (%) | Precision | Recall | F1-score |
| A–E | 50 | RF | 100 | 1.0 | 1.0 | 1.0 | B–E | 100 | RF | 100 | 1.0 | 1.0 | 1.0 | AB–CD–E | 700 | Bagged k-NN | 99 | 0.99 | 0.99 | 0.99 | AB–C–D–E | 1000 | LR | 94.4 | 0.95 | 0.94 | 0.94 | A–B–C–D–E | 3367 | SVM | 93.6 | 0.94 | 0.94 | 0.94 | Ictal vs. Preictal | 500 | Ensemble | 97.1 | 0.97 | 0.97 | 0.97 |
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