Int J Neural Syst - Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction.

Tópicos

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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.

Resumo Limpo

intrins timescal decomposit itd new nonlinear method timefrequ represent can deciph minut chang nonlinear eeg signal work automat classifi normal interict ictal eeg signal use featur deriv itd represent energi fractal dimens sampl entropi featur comput itd represent coupl decis tree classifi yield averag classif accuraci sensit specif respect use fold cross valid scheme applic nonlinear itd represent along conceptu advanc improv accuraci develop system clinic readi mass screen resourc constrain emerg economi scenario

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