Table 16 Summary of previous works for automated detection of normal and epileptic classes
Authors | Features | Classifier | Accuracy (%) |
|---|---|---|---|
Nigam and Graupe [35] | Nonlinear preprocessing filter | Diagnostic neural network | 97.20 |
Kannathal et al. [37] | Entropy measures adaptive neuro-fuzzy | Inference system (ANFIS) | 92.22 |
Srinivasan et al. [38] | Time andfrequency domain | Features Elman network | 99.60 |
Sadati et al. [39] | DWT adaptive neural | Fuzzy network | 85.90 |
Subasi [42] | DWT statistical measures | Mixture expert model (a modular neural network) | 94.50 |
Polat and Gunes [43] | FFT-based features | Decision tree | 98.72 |
Tzallas et al. [41] | Time-frequency methods | Artificial neural network | 97.72 to 100 |
Srinivasan et al. [40] | ApEn | Probabilistic neural network, Elman network | 100 |
Polat and Gunes [44] | FFT-based features | Artificial immune recognition system | 100 |
Polat and Gunes [45] | AR C4.5 | Decision tree classifier | 99.32 |
Ocak [46] | DWT-ApEn | Thresholding | 96.65 |
Guo et al. [47] | Relative wavelet energy | ANN | 95.85 |
Guo et al. [48] | ApEn and wavelet Transform | ANN | 99.85 |
Guo et al. [49] | Line length features and wavelet transform | ANN | 99.60 |
Subasi and Gursoy [51] | DWT-PCA, ICA, LDA | SVM | 98.75(PCA) |
99.50(ICA) | |||
100(LDA) | |||
Ubeyli [52] | AR | SVM | 99.56 |
Lima et al. [53] | Wavelet transform | SVM | 100 |
Guo et al. [50] | Genetic programming based | KNN | 99 |
Wang et al. [54] | Wavelet packet entropy | KNN | 100 |
Iscan et al. [55] | Cross correlation and PSD | Several classifiers including SVM | 100 |
Proposed method by authors Harikumar | dB2 wavelet hard thresholding | SVD | 98.03 |