Research Article

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

Table 9

Comparison with previous works.

Authors (year)ClassifierFeature extractionClassesSubsetsAccuracy (%)

Tang and Durand [10] (2012)SVMFilter bank, Teager energy, power, Lempel–Ziv complexity2(A, D), E98.72
Song et al. [22] (2016)Initial ELMDWT, Mahalanobis distance, sample entropy2D, E97.53
Güler et al. [29] (2005)ANNLyapunov exponents3A, D, E96.79
Liang et al. [30] (2010)SVMPrincipal component analysis, approximate entropy, power3A, D, E98.67
Murugavel and Ramakrishnan [8] (2016)SVMDWT, largest Lyapunov exponent, approximate entropy3A, D, E96
Riaz et al. [31] (2016)SVMEmpirical mode decomposition, temporal, spectral features3A, D, E85
This workSELMLDWT, maximum, standard deviation3A, D, E98.4