The Sensitivity of Scalp EEG at Detecting Seizures-A Simultaneous Scalp and Stereo EEG Study

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.

The impact of simultaneous intracranial recordings on scalp EEG: A finite element analysis

BACKGROUND

In this study, we examined the utility of simultaneous scalp and stereotactic intracranial electroencephalography (SSIEEG) in epilepsy patients. Although SSIEEG offers valuable insights into epilepsy and cognitive function, its routine use is uncommon. Challenges include interpreting post-craniotomy scalp EEG due to surgically implanted electrodes.

NEW METHOD

We describe our methodology for conducting SSIEEG recordings. To simulate the potential impact on EEG interpretation, we computed the leadfield of scalp electrodes with and without burrholes using Finite Element Analysis to compare the resulting sensitivity volume and waveforms of simulated intracranial signals between skulls with and without burrholes.

RESULTS

The presence of burr holes in the skull layer of the leadfield models did not discernibly modify simulated waveforms or scalp EEG topology. Using realistic SEEG burr hole diameter, the difference in the average leadfield of scalp electrodes was 0.12% relative to the effect of switching two nearby electrodes, characterized by the cosine similarity difference. No patients experienced adverse events related to SSIEEG.

COMPARISON WITH EXISTING METHODS

Although there is increasing acceptance and interest in SSIEEG, few studies have characterized the technical feasibility. Here, we demonstrate through modelling that scalp recordings from SSIEEG are comparable to that through an intact skull.

CONCLUSION

The placement and simultaneous acquisition of scalp EEG during invasive monitoring through stereotactically inserted EEG electrodes is routinely performed at the Hospital for Sick Children. Scalp EEG recordings may assist with clinical interpretation. Burr holes in the skull layer did not discernibly alter EEG waveforms or topology.

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE

Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients.

METHODS

We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies.

RESULTS

A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test).

SIGNIFICANCE

Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Correspondence between scalp-EEG and stereoelectroencephalography seizure-onset patterns in patients with MRI-negative drug-resistant focal epilepsy

OBJECTIVE

Our objective was to evaluate the relationship between scalp-EEG and stereoelectroencephalography (SEEG) seizure-onset patterns (SOP) in patients with MRI-negative drug-resistant focal epilepsy.

METHODS

We analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video-EEG followed by SEEG. We defined five types of SOPs on scalp-EEG and eight types on SEEG. We examined how various clinical variables affected scalp-EEG SOPs.

RESULTS

The most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp-EEG was always seen without delay from clinical onset and correlated with the presence of low-voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp-EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome.

SIGNIFICANCE

In patients with MRI-negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge.

PLAIN LANGUAGE SUMMARY

We looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia.

Impact of intracranial subclinical seizures on seizure outcomes after SLAH in patients with mesial temporal lobe epilepsy

OBJECTIVE

To investigate the association between subclinical seizures detected on intracranial electroencephalographic (i-SCSs)recordings and mesial temporal sclerosis (MTS), as well as their impact on surgical outcomes of stereotactic laser amygdalohippocampotomy (SLAH).

METHODS

A retrospective review was conducted on 27 patients with drug-resistant mesial temporal lobe epilepsy (MTLE) who underwent SLAH. The number of seizures detected on scalp EEG and iEEG was assessed. Patients were followed for a minimum of 3 years after SLAH.

RESULTS

Of the 1715 seizures recorded from mesial temporal regions, 1640 were identified as i-SCSs. Patients with MTS were associated with favorable short- and long-term surgical outcomes. Patients with MTS had a higher number of i-SCSs compared to patients without MTS. The numbers of i-SCSs were higher in patients with Engel I-II outcomes, but no significant statistical difference was found. However, it was observed that patients with MTS who achieved Engel I-II classification had higher numbers of i-SCSs than patients without MTS (P < 0.05).

CONCLUSION

Patients with MTS exhibited favorable short-term and long-term surgical outcome after SLAH. A higher number of i-SCSs was significantly associated with MTS in patients with MTLE. The number of i-SCSs tended to be higher in patients with Engel Ⅰ-Ⅱ surgical outcomes.

SIGNIFICANCE

The association between i-SCSs, MTS, and surgical outcomes in MTLE patients undergoing SLAH has significant implications for understanding the underlying mechanisms and identifying potential therapeutic targets to enhance surgical outcomes.

Analysis of Electrocorticography in Epileptic Patients With Responsive Neurostimulation Undergoing Scalp Electroencephalography Monitoring

PURPOSE

To describe the relationship of electrocorticography events detected by a brain-responsive neurostimulation system (RNS) and their association with ictal and interictal activity detected on simultaneous scalp EEG.

METHODS

We retrospectively identified patients with drug-resistant epilepsy implanted with RNS who subsequently underwent long-term scalp EEG monitoring. RNS detections were correlated to simultaneous activity recorded on scalp EEG to determine the characteristics of electrocorticography-stored long episodes associated with seizures or other findings on scalp EEG.

RESULTS

Eleven patients were included with an average of 3.6 days of monitoring. Most RNS detections were of very brief duration (<10 seconds, 92.9%) and received one stimulation therapy (80.8%). A high proportion of long episodes (67.1%) were not identified as electrographic seizures on scalp EEG. Of those ictal-appearing (71.2%) long episodes, 68.2% had seizure correlates. Long episodes associated with seizures on scalp EEG had a longer median duration compared with those without (39.7 vs. 16.8 seconds, P < 0.002) and had broader spread pattern and were of higher amplitude on electrocorticography. Brief potentially ictal rhythmic discharges were the most common EEG findings associated with long episodes that did not have scalp EEG seizure correlates (100% for ictal- and 50% for non-ictal-appearing long episodes).

CONCLUSIONS

Longer, broader spread and higher amplitude intracranial RNS detections are more likely to manifest as electrographic seizures on scalp EEG. Brief potentially ictal rhythmic discharges may serve as a scalp EEG biomarker of ictal intracranial episodes that are detected as long episodes by the RNS but not identified as electrographic seizures on scalp EEG.

Rolling in the Deep: Surface EEG Seizures Viewed Through the Lens of Stereo EEG

Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings Ferrand M, Baumann C, Aron O, Vignal JP, Jonas J, Tyvaert L, Colnat-Coulbois S, Koessler L, Maillard L. Neurology . 2023 Mar 24:10.1212/WNL.0000000000207135 . doi:10.1212/WNL.0000000000207135 . Epub ahead of print. PMID: 36963841. Background and objectives: It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intra-cerebral seizure onset in pre-surgical evaluation of drug resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and Stereo-EEG. Methods: We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least one seizure, in the epileptology unit in Nancy, France. We analyzed one seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intra-cerebral correlates. Results: We enrolled 129 patients in this study. The hierarchical cluster analysis showed six profiles on scalp EEG first modification. None was specific to a single intra-cerebral localization. The “normal EEG” and “blurred EEG” clusters (early muscle artifacts) comprised only five patients each and corresponded to no preferential intra-cerebral localization. The “temporal discharge” cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intra-cerebral localization. The “posterior discharge” cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The “diffuse suppression” cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the “frontal discharge” cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or pre-ictal spike on scalp and corresponded to preferential ventrodorsal frontal intra-cerebral localizations. Discussion: Hierarchical cluster analysis identified six seizure profiles regarding the first abnormality on scalp EEG. None of them was specific of a single intra-cerebral localization. Nevertheless, the strong relationships between the “temporal”, “frontal”, “diffuse suppression” and “posterior” profiles and intra-cerebral discharges localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intra-cerebral seizure onset.

Ambulatory video EEG extended to 10 days: A retrospective review of a large database of ictal events

OBJECTIVE

This work aims to determine the ambulatory video electroencephalography monitoring (AVEM) duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings.

METHODS

Patients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: differential diagnosis, seizure type classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion.

RESULTS

The referring clinical question was not answered entirely by the first event in 29.6% (n = 227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days.

CONCLUSIONS

At least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 h or single event recording cut-offs may be inadequate to adequately answer these three indications in a substantial proportion of patients.

SIGNIFICANCE

Extended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on video-EEG.

Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings

BACKGROUND AND OBJECTIVES

It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intracerebral seizure onset in presurgical evaluation of drug-resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and stereo-EEG.

METHODS

We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least 1 seizure in the epileptology unit in Nancy, France. We analyzed 1 seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intracerebral correlates.

RESULTS

We enrolled 129 patients in this study. The hierarchical cluster analysis showed 6 profiles on scalp EEG first modification. None were specific to a single intracerebral localization. The "normal EEG" and "blurred EEG" clusters (early muscle artifacts) comprised only 5 patients each and corresponded to no preferential intracerebral localization. The "temporal discharge" cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intracerebral localization. The "posterior discharge" cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The "diffuse suppression" cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the "frontal discharge" cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or preictal spike on scalp and corresponded to preferential ventrodorsal frontal intracerebral localizations.

DISCUSSION

The hierarchical cluster analysis identified 6 seizure profiles regarding the first abnormality on scalp EEG. None of them were specific of a single intracerebral localization. Nevertheless, the strong relationships between the "temporal," "frontal," "diffuse suppression," and "posterior" profiles and intracerebral discharge localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intracerebral seizure onset.

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.