J Med Syst - Classification of epilepsy using high-order spectra features and principle component analysis.

Tópicos

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Resumo

The classification of epileptic electroencephalogram (EEG) signals is challenging because of high nonlinearity, high dimensionality, and hidden states in EEG recordings. The detection of the preictal state is difficult due to its similarity to the ictal state. We present a framework for using principal components analysis (PCA) and a classification method for improving the detection rate of epileptic classes. To unearth the nonlinearity and high dimensionality in epileptic signals, we extract principal component features using PCA on the 15 high-order spectra (HOS) features extracted from the EEG data. We evaluate eight classifiers in the framework using true positive (TP) rate and area under curve (AUC) of receiver operating characteristics (ROC). We show that a simple logistic regression model achieves the highest TP rate for class "preictal" at 97.5% and the TP rate on average at 96.8% with PCA variance percentages selected at 100%, which also achieves the most AUC at 99.5%.

Resumo Limpo

classif epilept electroencephalogram eeg signal challeng high nonlinear high dimension hidden state eeg record detect preictal state difficult due similar ictal state present framework use princip compon analysi pca classif method improv detect rate epilept class unearth nonlinear high dimension epilept signal extract princip compon featur use pca highord spectra hos featur extract eeg data evalu eight classifi framework use true posit tp rate area curv auc receiv oper characterist roc show simpl logist regress model achiev highest tp rate class preictal tp rate averag pca varianc percentag select also achiev auc

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