J Med Syst - Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

Resumo Limpo

studi intend analyz electroencephalographi eeg signal wavelet transform wt diagnosi epilepsi employ various artifici neural network ann signal automat classif furthermor carri perform comparison aim three eeg signal decompos frequenc sub band wt featur vector extract sub band order reduc size extract featur vector princip compon analysi pca method appli necessari featur vector classifi five differ ann either epilept healthi perform evalu carri conduct roc analysi use ann model comparison also includ

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