Classification of inter-ictal and ictal EEGs using multi-basis MODWPT, dimensionality reduction algorithms and LS-SVM: A comparative study

Wavelet-based feature extraction techniques are very promising for classifying epileptic electroencephalograph (EEG) but the determination of the optimal wavelet basis is intractable. To overcome this strait, maximal overlap discrete wavelet package transform (MODWPT) was introduced to characterize epileptic EEG for the first time and the multi-basis MODWPT-based feature extraction was further proposed in this study. Instead of merely using single wavelet basis, multiple bases were synchronously adopted in one-time implementation of multi-basis MODWPT. Six dimensionality reduction algorithms, namely principal component analysis (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA), isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE), were then employed to map the extracted features into other domain in feature selection procedure. Finally, the mapped features were fed into a least squares support vector machine (LS-SVM) for classification and multiple kernel learning support vector machine (MKLSVM) was used as comparison. Experimental results show the combination of multi-basis MODWPT, ICA and LS-SVM with linear kernel provides the highest accuracy of 99.67% in classifying inter-ictal and ictal EEGs while MKLSVM in conjunction with multi-basis MODWPT-based PCA also leads to the maximal accuracy of 99.83%. Our presented scheme yields superior performance than the majority of newly-developed methods and is proven useful.