Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy

Publication date: Available online 3 October 2016
Source:Clinical Neurophysiology
Author(s): Anne H. Mooij, Birgit Frauscher, Mina Amiri, Willem M. Otte, Jean Gotman
ObjectiveTo assess whether there is a difference in the background activity in the ripple band (80-200 Hz) between epileptic and non-epileptic channels, and to assess whether this difference is sufficient for their reliable separation.MethodsWe calculated mean and standard deviation of wavelet entropy in 303 non-epileptic and 334 epileptic channels from 50 patients with intracerebral depth electrodes and used these measures as predictors in a multivariable logistic regression model. We assessed sensitivity, positive predictive value (PPV) and negative predictive value (NPV) based on a probability threshold corresponding to 90% specificity.ResultsThe probability of a channel being epileptic increased with higher mean (p=0.004) and particularly with higher standard deviation (p<0.0001). The performance of the model was however not sufficient for fully classifying the channels. With a threshold corresponding to 90% specificity, sensitivity was 37%, PPV was 80%, and NPV was 56%.ConclusionsA channel with a high standard deviation of entropy is likely to be epileptic; with a threshold corresponding to 90% specificity our model can reliably select a subset of epileptic channels.SignificanceMost studies have concentrated on brief ripple events. We showed that background activity in the ripple band also has some ability to discriminate epileptic channels.



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