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Updated: Aug 14, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
Seizure anticipation, states of consciousness and marginal predictability in temporal lobe epilepsy
Dingzhou Li1, Weiping Zhou, Ivo Drury
1Physics Department, University of Michigan, Ann Arbor, MI 48109, USA.
Purpose:
It has recently been shown that differences between the marginal predictability associated with scalp electrodes adjacent to and remote from the site of a seizure focus are able to distinguish between epochs temporally distant from and just prior to (within about 20 min) the onset of a seizure in patients with temporal lobe epilepsy. The purpose of this paper is to show that these differences of marginal predictability intervals are independent of the state of consciousness of the patient.
Methods:
We have studied a data set encompassing 33 preictal epochs (within 1 h prior to a seizure) and 61 interictal epochs (defined as at least 1 h away from any seizure) from 14 patients. Each 30 s interval of each epoch was categorized into one of seven different states of consciousness. Statistical models were used to search for relationships (in aggregated data) between the values of differences of marginal predictabilities and state of consciousness.
Results:
It was not possible to reject the null hypothesis of no relationship between differences of marginal predictabilities and state of consciousness.
Conclusions:
The values of the differences between marginal predictabilities on aggregated data are apparently insensitive to the state of consciousness. This conclusion, coupled with the fact that the differences between marginal predictabilities do depend on time to seizure, suggests the potential utility of these measures as the basis for ambulatory, non-invasive methods of seizure anticipation. However, the development of a practical non-invasive method for seizure anticipation requires further extensive study on disaggregated data from individual patients.
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