Comput Methods Programs Biomed - A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals.

Tópicos

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Resumo

In order to predict epileptic seizures many precursory features, extracted from the EEG signals, have been introduced. Before checking out the performance of features in detection of pre-seizure state, it is required to see whether these features are accurately extracted. Evaluation of feature estimation methods has been less considered, mainly due to the lack of a ground truth for the real EEG signals' features. In this paper, some simulated long-term depth-EEG signals, with known state spaces, are generated via a realistic neural mass model with physiological parameters. Thanks to the known ground truth of these synthetic signals, they are suitable for evaluating different algorithms used to extract the features. It is shown that conventional methods of estimating correlation dimension, the largest Lyapunov exponent, and phase coherence have non-negligible errors. Then, a parameter identification-based method is introduced for estimating the features, which leads to better estimation results for synthetic signals. It is shown that the neural mass model is able to reproduce real depth-EEG signals accurately; thus, assuming this model underlying real depth-EEG signals, can improve the accuracy of features' estimation.

Resumo Limpo

order predict epilept seizur mani precursori featur extract eeg signal introduc check perform featur detect preseizur state requir see whether featur accur extract evalu featur estim method less consid main due lack ground truth real eeg signal featur paper simul longterm deptheeg signal known state space generat via realist neural mass model physiolog paramet thank known ground truth synthet signal suitabl evalu differ algorithm use extract featur shown convent method estim correl dimens largest lyapunov expon phase coher nonneglig error paramet identificationbas method introduc estim featur lead better estim result synthet signal shown neural mass model abl reproduc real deptheeg signal accur thus assum model under real deptheeg signal can improv accuraci featur estim

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