Elsevier

The Lancet

Volume 357, Issue 9251, 20 January 2001, Pages 183-188
The Lancet

Articles
Anticipation of epileptic seizures from standard EEG recordings

https://doi.org/10.1016/S0140-6736(00)03591-1Get rights and content

Summary

Background

New methods derived from non-linear analysis of intracranial recordings permit the anticipation of an epileptic seizure several minutes before the seizure. Nevertheless, anticipation of seizures based on standard scalpelectroencephalographical (EEG) signals has not been reported yet. The accessibility to preictal changes from standard EEGs is essential for expanding the clinical applicability of these methods.

Methods

We analysed 26 scalp-EEG/video recordings, from 60 min before a seizure, in 23 patients with temporal-lobe epilepsy. For five patients, simultaneous scalp and intracranial EEG recordings were assessed. Long-term changes before seizure onset were identified by a measure of non-linear similarity, which is very robust in spite of large artifacts and runs in real-time.

Findings

In 25 of 26 recordings, measurement of non-linear changes in EEG signals allowed the anticipation of a seizure several minutes before it occurred (mean 7 min). These preictal changes in the scalp EEG correspond well with concurrent changes in depth recordings.

Interpretation

Scalp-EEG recordings retain sufficient dynamical information which can be used for the analysis of preictal changes leading to seizures. Seizure anticipation strategies in real-time can now be envisaged for diverse clinical applications, such as devices for patient warning, for efficacy of ictal-single photon emission computed tomography procedures, and eventual treatment interventions for preventing seizures.

Introduction

In most patients with epilepsy, seizures occur suddenly and without identifiable external precipitants. The unpredictability of seizure onset is a major threat for people with uncontrolled epilepsy and a cause of disability1 and mortality.2 Prediction of seizure onset, even in the shortterm, would provide time for the application of preventive measures to keep the risk of seizure to a minimum and, ultimately, improve quality of life.

Intracranial recordings of patients who are candidates for surgical treatment offers the most precise access to the emergence of a seizure.3 Ways to anticipate seizure onset before the first intracranial electrical changes have been intensively investigated by conventional linear (ie, frequency) analyses.4, 5, 6 Nevertheless, prediction does not exceed more than a few seconds before visual detection of the seizure. Non-linear analysis is an alternative way to characterise qualitative changes in the dynamics of complex systems7 and could be important in clinical practice.8 Applied to intracranial recordings of patients with temporal-lobe epilepsy (TLE), these methods have shown that the evolution toward a seizure involves not just two states-interictal and ictal-but also a preictal transitional phase of several minutes that is not detected by linear methods.9, 10, 11 This preictal transitional phase could become the basis to anticipate seizures in clinical applications.

To make seizure anticipation practical in real life conditions, and to study types of epilepsy that do not warrant intracranial implantation, we analysed scalpelectroencephalographical (ECG) recordings. Scalp EEG, however, is well known to be subject to signal attenuation, poor spatial resolution, and noise or artifacts,12 which may render delicate13 and even questionable14 the detection of changes with current non-linear measures, originally designed to analyse systems with little noise.

To improve non-linear analysis we have proposed a strategy well suited to track dynamical changes in complex brain signals,11, 15 which measures similarity of EEG dynamics between recordings taken at distant moments in time. This relative measure indicates changes in brain electrical activity with greater accuracy than other nonlinear techniques.16, 17 Furthermore, it has the advantage of being very robust against noise and artifacts, and is fast enough to be carried out in real-time.

In the present study, we have applied this non-linear strategy to analyse scalp-EEG recordings from patients with TLE to assess whether changes in brain dynamics can be detected early enough to anticipate the onset of the clinical seizure. In a subgroup of patients, we validated our results on simultaneous surface and intracranial recordings.

Section snippets

Scalp EEG recordings

We studied a sample of 18 patients with refractory TLE who required continuous EEG recording and video monitoring to localise seizure onsets. Hippocampal sclerosis as defined by magnetic resonance imaging was the most frequent pathological condition associated with TLE (table). Patients were free to move in the recording room.

To avoid changes induced by variation of arousal states,5 only seizures in which the patients were awake (14 seizures) or asleep (four seizures) for the entire preictal

Results

Figure 2 shows a representative example of the results for simultaneous intracranial and scalp recording 50 min before a spontaneous seizure of left-temporal origin. The similarity profiles, relative to a reference state taken at the beginning of the recording, are depicted for selected scalp and depth channels in the left-temporal region (figure 2A). A persistent deviation from the reference state is recorded around 18 min before the seizure in both the scalp and intracranial similarity

Discussion

Our results indicate that preseizure changes in brain dynamics can be detected from recordings of scalp-EEG activity. These changes were characterised with a reference state taken 1 h before the ictus, and were detected several minutes before the earliest clinical or overt EEG manifestations of the seizure. We chose this reference state to analyse only preictal epochs recorded during stable states of wakefulness or during sleep, to avoid changes in the interictal dynamics induced by major

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