Feature extraction and recognition of ictal EEG using EMD and SVM
Citations
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Cites methods from "Feature extraction and recognition ..."
...These features are the mean frequency of IMFs computed from the Fourier–Bessel series expansion [31], the area computed from the analytic signal representation (ASR) of the IMFs [32,33], the 95% confidence ellipse area of the second-order difference plot (SODP) of IMFs [33,34], the 95% confidence ellipse area and interquartile range (IQR) of the Euclidean distances parameters extracted from the 2D and 3D phase space representation (PSR) of IMFs [35], the histogram-based features extracted from time-frequency images obtained using the Hilbert–Huang transform [36], multi-level local patterns [37], the coefficient of variation and the fluctuation index computed from IMFs [38], etc....
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...The decision function of the LS-SVM classifier can be expressed as [35,38]:...
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Cites methods from "Feature extraction and recognition ..."
...Higher order statistics of IMFs [30], geometrical properties of the decomposed IMF in complex plane [31], and the variation and fluctuation of IMF [32] are used as features for seizure prediction and detection....
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208 citations
Cites methods from "Feature extraction and recognition ..."
...The LS-SVM classifier decision function can be determined as [27], [38]...
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References
37,861 citations
"Feature extraction and recognition ..." refers background or methods in this paper
...The OSH can classify both the training samples and the unseen samples in the test set with the minimum risk of misclassification [17,18]....
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...Support Vector Machine (SVM) was introduced by Vapnik and his co-workers [17] as a very effective method for general pattern classification....
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18,956 citations
2,387 citations
2,304 citations
"Feature extraction and recognition ..." refers background in this paper
...It shifts out the fastest changing component of a composite signal first [20]....
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843 citations
"Feature extraction and recognition ..." refers background in this paper
...The OSH can classify both the training samples and the unseen samples in the test set with the minimum risk of misclassification [17,18]....
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