J Med Syst - Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ case(1353) use(1143) diagnosi(1136) }
{ control(1307) perform(991) simul(935) }
{ model(2341) predict(2261) use(1141) }
{ patient(1821) servic(1111) care(1106) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ treatment(1704) effect(941) patient(846) }
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{ sampl(1606) size(1419) use(1276) }
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{ activ(1138) subject(705) human(624) }
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{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.

Resumo Limpo

studi intend perform automat classif electroencephalographi eeg signal via artifici neural network ann investig signal use wavelet transform wt diagnos epilepsi syndrom eeg signal decompos frequenc subband use wt set featur vector extract subband dimens featur vector reduc via princip compon analysi pca method classifi epilept healthi use multilay perceptron mlp elman ann perform evalu use ann model carri perform receiv oper characterist roc analysi

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