Automatic feature extraction using genetic programming: An application to epileptic EEG classification
Citations
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601 citations
Cites methods from "Automatic feature extraction using ..."
...60% using wavelet transform and line length feature [42], and an accuracy of 99% using genetic programming based features in a K-Nearest Neighbor (KNN) classifier [43]....
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449 citations
Cites methods from "Automatic feature extraction using ..."
...Examples of classification algorithms used for seizure detection and epilepsy diagnosis are: k-Nearest Neighbor algorithm (k-NN) [31], Probabilistic Neural Network (PNN) [32], Fisher’s linear discriminant (FLD) [33], Support Vector Machine (SVM) [34], Optimum Path Forest (OPF) [35], Principal Component Analysis (PCA) [36], and Enhanced Probabilistic Neural Network (EPNN) [37]....
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...Employing nonlinear dynamics and chaos theory researchers have extracted various nonlinear features such as entropies [24], energy [25], correlation dimension [47], fractal dimension [47,27], Lyapunov exponent [47], Higher Order Spectra (HOS) [28,25] from both detailed and approximate coefficients of the WT and used them for signal classification and seizure detection and epilepsy diagnosis [29,30,31]....
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Cites background from "Automatic feature extraction using ..."
...Researchers have attempted various classifiers namely artificial neural network [16]–[19], support vector machines [8], [20]–[23], k-nearest neighbor (k-NN) [24], [25], quadratic analysis [26], logistic regression [6], [13], naïve Bayes (NB) [13], decision tree [13], [27], Gaussian mixture model [2], [25], adaptive neuro-fuzzy inference systems [20], [31], mixture of expert model [28]–[30], surrogate data analysis [12], [32], learning vector quantization [33], Markov modeling [34] to classify the epileptic seizure abnormality from the EEG data....
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References
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