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

Anne H Mooij, Birgit Frauscher, Mina Amiri, Wim Otte, Jean Gotman

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

OBJECTIVE: To assess whether there is a difference in the background activity in the ripple band (80-200Hz) 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.

Original languageEnglish
Pages (from-to)3529-3536
Number of pages8
JournalClinical Neurophysiology
Volume127
Issue number12
DOIs
Publication statusPublished - Dec 2016

Keywords

  • Epilepsy
  • Intracerebral EEG
  • High frequency activity
  • Wavelet entropy

Fingerprint

Dive into the research topics of 'Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy'. Together they form a unique fingerprint.

Cite this