Focal epilepsy without interictal spikes on scalp EEG: A common finding of uncertain significance

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring

OBJECTIVES

Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question.

METHODS

Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events).

RESULTS

Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures.

CONCLUSION

Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

Association Between Magnetoencephalography-Localized Epileptogenic Zone, Surgical Resection Volume, and Postsurgical Seizure Outcome

PURPOSE

Surgical resection of magnetoencephalography (MEG) dipole clusters, reconstructed from interictal epileptiform discharges, is associated with favorable seizure outcomes. However, the relation of MEG cluster resection to the surgical resection volume is not known nor is it clear whether this association is direct and causal, or it may be mediated by the resection volume or other predictive factors. This study aims to clarify these open questions and assess the diagnostic accuracy of MEG in our center.

METHODS

We performed a retrospective cohort study of 68 patients with drug-resistant epilepsy who underwent MEG followed by resective epilepsy surgery and had at least 12 months of postsurgical follow-up.

RESULTS

Good seizure outcomes were associated with monofocal localization (χ2 = 6.94, P = 0.001; diagnostic odds ratio = 10.2) and complete resection of MEG clusters (χ2 = 22.1, P < 0.001; diagnostic odds ratio = 42.5). Resection volumes in patients with and without removal of MEG clusters were not significantly different (t = 0.18, P = 0.86; removed: M = 20,118 mm3, SD = 10,257; not removed: M = 19,566 mm3, SD = 10,703). Logistic regression showed that removal of MEG clusters predicts seizure-free outcome independent of the resection volume and other prognostic factors (P < 0.001).

CONCLUSIONS

Complete resection of MEG clusters leads to favorable seizure outcomes without affecting the volume of surgical resection and independent of other prognostic factors. MEG can localize the epileptogenic zone with high accuracy. MEG interictal epileptiform discharges mapping should be used whenever feasible to improve postsurgical seizure outcomes.

Assessing 72 h vs. 24 h of long-term video-EEG monitoring to confirm the diagnosis of epilepsy: a retrospective observational study

Introduction

Paroxysmal seizure-like events can be a diagnostic challenge. Inpatient video-electroencephalography (EEG) monitoring (VEM) can be a valuable diagnostic tool, but recommendations for the minimal duration of VEM to confirm or rule out epilepsy are inconsistent. In this study, we aim to determine whether VEM of 48 or 72 h was superior to 24 h.

Methods

In this monocentric, retrospective study, we included 111 patients with paroxysmal, seizure-like events who underwent at least 72 h of VEM. Inclusion criteria were as follows: (1) Preliminary workup was inconclusive; (2) VEM admission occurred to confirm a diagnosis; (3) At discharge, the diagnosis of epilepsy was conclusively established. We analyzed the VEM recordings to determine the exact time point of the first occurrence of epileptic abnormalities (EAs; defined as interictal epileptiform discharges or electrographic seizures). Subgroup analyses were performed for epilepsy types and treatment status.

Results

In our study population, 69.4% (77/111) of patients displayed EAs during VEM. In this group, the first occurrence of EAs was observed within 24 h in 92.2% (71/77) of patients and within 24-72 h in 7.8% (6/77). There was no statistically significant difference in the incidence of EA between medicated and non-medicated patients or between focal, generalized epilepsies and epilepsies of unknown type. Of the 19 recorded spontaneous electroclinical seizures, 6 (31.6%) occurred after 24 h.

Discussion

A VEM of 24 h may be sufficient in the diagnostic workup of paroxysmal seizure-like events under most circumstances. Considering the few cases of first EA in the timeframe between 24 and 72 h, a prolonged VEM may be useful in cases with a high probability of epilepsy or where other strategies like sleep-EEG or ambulatory EEG show inconclusive results. Prolonged VEM increases the chance of recording spontaneous seizures. Our study also highlights a high share of subjects with epilepsy that do not exhibit EAs during 72 h of VEM.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings

OBJECTIVE

This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom.

METHODS

We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction.

RESULTS

Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes.

SIGNIFICANCE

This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Diagnostic yield of seizure recordings and neuroimaging in patients with focal epilepsy without interictal epileptiform discharges

OBJECTIVE

Repeated routine electroencephalography (EEG) or even long-term video-EEG monitoring (VEM) does not always record interictal epileptiform discharges (IEDs) in some patients with epilepsy. The present study investigated whether focal seizures detected by VEM and focal abnormalities on neuroimaging are useful for the diagnosis of patients with focal epilepsy without IEDs.

METHODS

We retrospectively reviewed 409 consecutive patients with focal epilepsy (207 men, aged 9 to 76 years) who underwent 4- or 5-day VEM, magnetic resonance imaging (MRI), and fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) for diagnosis to identify patients without IEDs. The occurrence of focal seizures during VEM and the presence of focal abnormalities on neuroimaging were investigated in those patients. The occurrence rate of seizures during VEM was investigated in patients with daily, weekly, monthly, and yearly seizure frequency based on history-taking.

RESULTS

Ninety-five (23.2%) of 409 patients with focal epilepsy did not have IEDs. Fifty-five (57.9%) of the 95 patients had focal seizures during VEM. Fifty-four patients (56.8%) showed focal abnormalities compatible with seizure semiology on neuroimaging investigations. Neither seizure recordings nor neuroimaging abnormalities were seen in 16 (16.8%) of the 95 patients. The occurrence rate of seizures during VEM depended on the seizure frequency at history-taking. However, 28 (45.9%) of 61 patients with monthly and yearly seizure frequency had focal seizures during 4- or 5-day VEM with seizure induction.

CONCLUSIONS

Video-EEG monitoring can detect focal seizures in patients with focal epilepsy but no IEDs. Comprehensive assessment including VEM and neuroimaging study is important for the diagnosis.

Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter H extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.

EEG Source Imaging (ESI) utility in clinical practice

Epilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73-91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.