The Yield of Ambulatory EEG-Video Monitoring

Home-video EEG monitoring in a pediatric setting

Introduction

Pediatric video-EEG monitoring is a standard procedure in epilepsy clinics, typically conducted in in-hospital settings.However, hospitalizationis sometimesunnecessary and imposes a burden on children and their families. In response to the rise of telehealth, home video-EEG monitoring has emerged, utilizing portable EEG equipment and video-cameras.

Objective

The aim of this study was to assess the feasibility of home video-EEGin a pediatric population.

Methods

We conducteda prospective pilot study of twentyhome video-EEG tests in children. We evaluated the quality of EEG and video recordings using a 5-point scale.Demographic, clinical and quality data were comparedto a similar group undergoing in-hospital video-EEG monitoring.

Results

Twenty children aged 2.1-17.2 years (mean 9.57 ± 1.01), 12 females (60 %), underwent home video-EEG. A higher proportion of children with intellectual disability/autism were observed in the home-EEG group compared to the in-hospital group: 12 patients (60 %) vs. 5 (25 %) (p < 0.05*, Fisher exact test). In the ambulatory group patients with developmental and epileptic encephalopathy were overrepresented (7 i.e., 35 % vs. 0), while those withself-limited childhood epilepsy were more prevalent in the in-hospital group (5 i.e., 25 % vs 0) (p < 0.05*, Chi square). In the ambulatory group the reasons for referral were seizure localization/classification in 11 patients (55 %), paroxysmal event classification in 5 (25 %) and quantification of sleep epileptic activity in 4(20 %),similar to the in-hospital group (40 %, 40 % and 20 % respectively, p > 0.05, Chi square). The quality of the EEG recording was higher compared to in-hospital tests: median 5 [IQR 3.25-5] vs 4[IQR 3-4] (p < 0.05*, Mann-Whitney U test), while the quality of video recording was lower compared to in-hospital recordings: median 3[IQR 2.25-4] vs 5[IQR4-5] (p < 0.01**, Mann-Whitney U test).

Conclusions

Home video-EEG monitoring is apromising option forlong-termpediatric EEG monitoring, particularlyfor children with special needs.

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.

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.

Impact of ambulatory EEG in the management of patients with epilepsy in resource-limited Latin American populations

Objective

Ambulatory electroencephalography (AEEG) monitoring allows for prolonged recordings in normal environments, such as patients' homes, and is recognized as a cost-effective alternative to inpatient long-term video-EEG primarily in resource-limited countries. We aim to describe the impact of AEEG on the assessment of patients with suspected or confirmed epilepsy in two independent Latin-American populations with limited resources.

Methods

We included 63 patients who had undergone an AEEG due to confirmed/suspected epilepsy. Clinical (demographic, current antiseizure medication and indication) and electroencephalographic (duration of the study, result, and impact on clinical decision-making) were reviewed and compared.

Results

The main indication for an AEEG was the differentiation of seizures from non-epileptic events with 57% of patients. It was categorized as positive in 36 patients and did have an impact on the clinical decision-making process in 57% of patients. AEEG captured clinical events in 35 patients (20 epileptic and 15 non-epileptic).

Conclusions

AEEG proves to be a valuable tool in resource-limited settings for assessing suspected or confirmed epilepsy cases, with a significant impact on clinical decisions.

Significance

Our study provides valuable insights into the use of AEEG in under-resourced regions, shedding light on the challenges and potential benefits of this tool in clinical practice.

The Yield of Ambulatory Video-EEG: Predictors of Successful Event Capture

Background and Objectives

The purpose of this study was to assess the likelihood of capturing a patient's typical event in question on ambulatory video-EEG monitoring (AVEM) based on certain baseline patient or event characteristics.

Methods

We retrospectively reviewed 300 studies that resulted between June 2021 and August 2022 ordered by adult epileptologists. Patients were included in event analysis if the study was ordered for the purpose of capturing an event (and excluded for all other purposes).

Results

A total of 149 studies were included in event analysis. Sixty-eight patients (46%) had their typical events captured on AVEM. Diagnosis was an epileptic seizure in 17 patients (25%), psychogenic nonepileptic seizure in 7 (10%), and other nonepileptic events in 44 (65%). Regarding event frequency, for patients who on average had daily events, 84% had events captured, which corresponds to a significantly increased odds ratio (OR 17.90, 95% CI 7.55-42.44, p < 0.001). For those who had events <1 per week to ≥1 per month, only 9% had events captured (OR 0.06, 95% CI 0.02-0.19, p < 0.001). No patients who had events less frequently than once per month had a diagnostic AVEM. Regarding the number of antiseizure medications (ASMs), the odds ratio was increased for those not on ASMs (OR 2.65, 95% CI 1.17 -6.03, p = 0.02) and decreased for those on 1 ASM (OR 0.28, 95% CI 0.13 -0.60, p = 0.001). There was no statistical significance based on event type (motor vs nonmotor), prior seizure diagnosis, history of psychiatric comorbidity, or presence of a focal brain lesion.

Discussion

Certain baseline characteristics can increase or decrease the pretest probability of capturing a typical event on AVEM, particularly the frequency of events and number of ASMs. This can be useful information for clinicians before ordering a study so that resources can be properly allocated.

Rates of event capture of home video EEG

OBJECTIVE

Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video electroencephalography (EEG) monitoring. This study was undertaken to measure the event capture rate of an home service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera.

METHODS

Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and physiological state.

RESULTS

6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean = 93.66%, median = 100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep.

CONCLUSIONS

Event capture was similar to previously reported rates from home studies, with higher capture rates on video. Most patients have all events captured on camera.

SIGNIFICANCE

Home monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.

Resting-State fMRI Can Detect Alterations in Seizure Onset and Spread Regions in Patients with Non-Lesional Epilepsy: A Pilot Study

Introduction

Epilepsy is defined as non-lesional (NLE) when a lesion cannot be localized via standard neuroimaging. NLE is known to have a poor response to surgery. Stereotactic electroencephalography (sEEG) can detect functional connectivity (FC) between zones of seizure onset (OZ) and early (ESZ) and late (LSZ) spread. We examined whether resting-state fMRI (rsfMRI) can detect FC alterations in NLE to see whether noninvasive imaging techniques can localize areas of seizure propagation to potentially target for intervention.

Methods

This is a retrospective study of 8 patients with refractory NLE who underwent sEEG electrode implantation and 10 controls. The OZ, ESZ, and LSZ were identified by generating regions around sEEG contacts that recorded seizure activity. Amplitude synchronization analysis was used to detect the correlation of the OZ to the ESZ. This was also done using the OZ and ESZ of each NLE patient for each control. Patients with NLE were compared to controls individually using Wilcoxon tests and as a group using Mann-Whitney tests. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DoC), and voxel-mirrored homotopic connectivity (VMHC) were calculated as the difference between NLE and controls and compared between the OZ and ESZ and to zero. A general linear model was used with age as a covariate with Bonferroni correction for multiple comparisons.

Results

Five out of 8 patients with NLE showed decreased correlations from the OZ to the ESZ. Group analysis showed patients with NLE had lower connectivity with the ESZ. Patients with NLE showed higher fALFF and ReHo in the OZ but not the ESZ, and higher DoC in the OZ and ESZ. Our results indicate that patients with NLE show high levels of activity but dysfunctional connections in seizure-related areas.

Discussion

rsfMRI analysis showed decreased connectivity directly between seizure-related areas, while FC metric analysis revealed increases in local and global connectivity in seizure-related areas. FC analysis of rsfMRI can detect functional disruption that may expose the pathophysiology underlying NLE.

Continuous Electroencephalogram Evaluation of Paroxysmal Events in Critically Ill Patients: Diagnostic Yield and Impact on Clinical Decision Making

BACKGROUND

Continuous electroencephalogram (cEEG) monitoring has been widely used in the intensive care unit (ICU) for the evaluation of patients in the ICU with altered consciousness to detect nonconvulsive seizures. We investigated the yield of cEEG when used to evaluate paroxysmal events in patients in the ICU and assessed the predictors of a diagnostic findings. The clinical impact of cEEG was also evaluated in this study.

METHODS

We identified patients in the ICU who underwent cEEG monitoring (> 6 h) to evaluate paroxysmal events between January 1, 2018, and December 31, 2019. We extracted patient demographics, medical history, neurological examination, brain imaging results, and the description of the paroxysmal events that necessitated the monitoring. We dichotomized the cEEG studies into those that captured habitual nonepileptic events or revealed epileptiform discharges (ictal or interictal), i.e., those considered to be of positive diagnostic yield (Y +), and those studies that did not show those findings (negative diagnostic yield, Y -). We also assessed the clinical impact of cEEG by documenting changes in administered antiseizure medication (ASM) before and after the cEEG.

RESULTS

We identified 159 recordings that were obtained for the indication of paroxysmal events, of which abnormal movements constituted the majority (n = 123). For the remaining events (n = 36), descriptions included gaze deviations, speech changes, and sensory changes. Twenty-nine percent (46 of 159) of the recordings were Y + , including the presence of ictal or interictal epileptiform discharges (n = 33), and captured habitual nonepileptic events (n = 13). A history of epilepsy was the only predictor of the study outcome. Detection of abnormal findings occurred within 6 h of the recording in most patients (30 of 46, 65%). Overall, cEEG studies led to 49 (31%) changes in ASM administration. The changes included dosage increases or initiation of ASM in patients with epileptiform discharges (n = 28) and reduction or elimination of ASM in patients with either habitual nonepileptic events (n = 5) or Y - cEEG studies (n = 16).

CONCLUSIONS

Continuous electroencephalogram monitoring is valuable in evaluating paroxysmal events, with a diagnostic yield of 29% in critically ill patients. A history of epilepsy predicts diagnostic studies. Both Y + and Y - cEEG studies may directly impact clinical decisions by leading to ASMs changes.

Ambulatory EEG: Crossing the divide during a pandemic

The COVID-19 pandemic forced temporary closure of epilepsy monitoring units across the globe due to potential hospital-based contagion. As COVID-19 exposures and deaths continues to surge in the United States and around the world, other types of long-term EEG monitoring have risen to fill the gap and minimize hospital exposure. AEEG has high yield compared to standard EEG. Prolonged audio-visual video-EEG capability can record events and epileptiform activity with quality like inpatient video-EEG monitoring. Technological advances in AEEG using miniaturized hardware and wireless secure transmission have evolved to small portable devices that are perfect for people forced to stay at home during the pandemic. Application of seizure detection algorithms and Cloud-based storage with real-time access provides connectivity to AEEG interpreters during prolonged "shut-down". In this article we highlight the benefits of AEEG as an alternative to diagnostic inpatient VEM during the paradigm shift to mobile heath forced by the Coronavirus.

How much time is enough? Establishing an optimal duration of recording for ambulatory video EEG

OBJECTIVE

Ambulatory video EEG allows for extended recording of EEG in the comfort of a patient's home. However, the optimal duration of recording to capture clinical events is yet to be established. The current study uses retrospective analyses to identify an optimal recording duration for at-home video EEG.

METHODS

A retrospective review was performed utilizing an anonymized database of ambulatory video EEG recordings performed between March and September 2020 with a national in-home EEG provider. Only completed assessments with neurologists' reads of raw data were reviewed, resulting in 3644 unique studies divided into three age cohorts: pediatrics (n = 941), adult (n = 2020), and geriatric (n = 683). Cohorts were characterized by assessment yield and time to first typical clinical event, as well as subsequent typical events over duration of recording.

RESULTS

Frequency distributions reveal over half of first events are captured within 12 hours, but longer recording durations capture a much wider majority of both first typical events, as well as the mean number of subsequent events (5 clinical events). In 72 hours, over 97% of first events were observed in adult and geriatric patients, as well as over 95% of the mean number of subsequent events. In children, time to first event was significantly earlier than either adult or geriatric samples, with 98% of first events, and 92.8% of the mean number of subsequent events being observed in 48 hours.

SIGNIFICANCE

These results from a large-scale, national dataset of patients using in-home EEG monitoring suggests recording at least 48 hours in duration for children, and at least 72 hours in duration for adult and geriatric samples, is optimal to maximize the likelihood of observing typical clinical events to facilitate diagnosis.

Value of smartphone videos for diagnosis of seizures: Everyone owns half an epilepsy monitoring unit

The diagnosis of epilepsy is primarily based on the history and the verbal description of the events in question. Smartphone videos are increasingly used to assist in the diagnosis. The purpose of this study is to evaluate their value for the diagnosis of seizures. We prospectively collected smartphone videos from patients who presented to our epilepsy center over two years. The video-based diagnosis was then compared to the eventual diagnosis based on video-electroencephalographic (EEG) monitoring with recorded episodes. Video-EEG studies and smartphone videos were reviewed by two separate physicians, each blinded to the other's interpretation. Fifty-four patients were included in the final analysis (mean age = 34.7 years, SD = 17 years). Data (either smartphone video or video-EEG monitoring) were inconclusive in 18 patients. Of the 36 patients with conclusive data, 34 (94%) were in agreement. Smartphone video interpretation can be a useful adjunctive tool in the diagnosis of seizure-like events.