Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy. Issue 12 (December 2016)

• Record Type: Journal Article
• Authors: Mooij, Anne H.; Frauscher, Birgit; Amiri, Mina; Otte, Willem M.; Gotman, Jean
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 12(2016:Dec.)
• Page Start: 3529
• Page End: 3536
• Publication Date: 2016-12
• Subjects: Epilepsy -- Intracerebral EEG -- High frequency activity -- Wavelet entropy; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.09.011
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Background activity in the ripple band is described by measures of wavelet entropy. A channel is more likely to be epileptic with high standard deviation of entropy. A model based on entropy measures can select a subset of the epileptic channels. Abstract: Objective: To 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. Methods: We 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. Results: The 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%. Conclusions: A 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. Significance: Most studies have concentrated on brief ripple events. We showed that background activity in the ripple band also has some ability to discriminate epileptic channels.