Detection of epileptic activity in presumably normal EEG

Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity

Objectively estimating disease severity and treatment success is a main problem in outpatient managing of epilepsy. Self-reported seizures diaries are well-known to underestimate the actual seizure count, and repeated EEGs might not show interictal epileptiform discharges (IEDs), although patients suffer from seizures. In this prospective study, we investigate the potential of microstate analysis to monitor epilepsy patients independently of their IED count. From our databank of candidates for epilepsy surgery, we included 18 patients who underwent controlled resting EEG sessions (with eyes closed, 30 min), at around the same time of the day, during at least four days (range: 4-8 days; mean: 5). Nine patients with temporal foci, six with extratemporal foci, and three with generalized epilepsy were included. Each patient's IEDs were marked and the topographic voltage maps of the IED peaks were averaged, and an individual average spike topography (AST) was created. The AST was then backfitted to each timepoint of the whole EEG resulting in the Spike-Microstate (SMS). The presence of the SMS in the residual EEG outside of the short IEDs epochs was determined for each recording session in each patient and correlated with the occurrence of the IEDs across all recording session, as well as with the drug charge of each day. Overall, SMS was much more represented in the routine EEG than the IEDs: they were identified 262 times more often than IEDs. The SMS time coverage correlated significantly with the IED occurrence rate (rho = 0.56; P < 0.001). If only patients with focal epilepsy were considered, this correlation was even higher rho = 0.69 (P < 0.001). Drug charge per day did not correlate with SMS. In this proof-of-concept study, the time coverage of SMS correlated strongly with the occurrence rate of the IEDs, they can be retrieved in the scalp EEG at a much higher occurrence rate. We conclude that SMS, once obtained for a given patient, are a more abundant marker of hidden epileptic activity than IEDs, in particular in focal epilepsy, and can be used also in absence of IEDs. Future larger studies are needed to verify its potential as monitoring tool and to determine cut-off values when drug protection becomes imperfect.

EEG biomarker candidates for the identification of epilepsy

Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.

Modulation in time of the interictal spiking pattern related to epileptic seizures

OBJECTIVE

To test the hypothesis that significant changes in the occurrence of interictal epileptiform electroencephalography (EEG) discharges (EDs) are associated with seizures: while some EDs are pro-convulsive, increasing at seizure-occurrence, others are protective, showing decrease related to seizures.

METHODS

We analyzed 102 consecutive, long-term video-EEG monitoring sessions, from 98 patients. Using a semi-automated spike-detection method, we quantified the occurrence of EDs, grouped according to their location and morphology (clusters) and we constructed graphical representation of data, showing changes in time of the spiking patterns (spike-histograms). We compared the spike-histograms with the time-points of the seizures (pre-, peri- and postictal changes).

RESULTS

Totally 179 ED-clusters were identified. Modulation of the spiking pattern, associated with seizures, was observed in 66 clusters (37%), from 47 patients (48%). Most of these changes (40 clusters; 61%) were related to increase in the spiking-pattern.

CONCLUSIONS

Changes in spiking-pattern were associated with more than one third of the EDs. Both increasing and decreasing patterns were observed.

SIGNIFICANCE

EDs are more often pro-convulsive, with increasing spiking patterns associated with seizures. However, in more than one third of the ED clusters modulated by seizures, the spiking pattern decreased, raising the possibility of an anticonvulsive function of these discharges.