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


CONCLUSIONS
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.
Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada; Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. Electronic address: A.H.Mooij-4@umcutrecht.nl.
Anne H
AH
Mooij
Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada; Department of Medicine and Center for Neuroscience Studies, Queen's University, Kingston, Ontario K7L 3N6, Canada. Electronic address: birgit.frauscher@i-med.ac.at.
Birgit
B
Frauscher
Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada. Electronic address: mina.amiri@mail.mcgill.ca.
Mina
M
Amiri
Brain Center Rudolf Magnus, Department of Pediatric Neurology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. Electronic address: W.M.Otte@umcutrecht.nl.
Willem M
WM
Otte
Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada. Electronic address: jean.gotman@mcgill.ca.
Jean
J
Gotman
D001921:Brain / Q000502:physiology / Q000503:physiopathology*; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D012189:Retrospective Studies; D058067:Wavelet Analysis
Epilepsy; High frequency activity; Intracerebral EEG; Wavelet entropy