Research Article
Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG
Table 2
Detection results of focal and nonfocal EEG signals of published journal articles using the Bern-Barcelona EEG database.
| Author (year) | Feature extraction methods | Classifier | Accuracy |
| Sharma et al. (2015) [21] | EMD, entropy | SVM | 87.0% | Sriraam et al. (2017) [22] | Statistical, frequency-based, entropy, FD, Wilcoxon test | SVM | 92.2% | Sharma et al. (2017) [23] | WFB, entropy, -test | LS-SVM | 94.3% | Das and Bhuiyan (2016) [24] | EMD-DWT, entropy | KNN | 89.4% | Bhattacharyya et al. (2017) [25] | EWT, RPS, CTM | LS-SVM | 90.0% | Gupta et al. (2017) [26] | FAWT, entropy, Kruskal-Wallis test | LS-SVM | 94.4% | Zhao et al. (2018) [27] | Entropy | CNN | 83.0% | Daoud and Bayoumi (2020) [28] | DCAE | MLP | 93.2% | TFCNN | STFT | 2d-CNN | 91.9% | Mixed-CNN | 1d convolution layer | 2d-CNN | 92.5% | TF-HybridNet | STFT, 1d convolution layer | 2d-CNN | 94.3% |
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