Ambulatory EEG Usefulness in Epilepsy Management

Clinical findings of long-term ambulatory video EEG following routine EEG

PURPOSE

This study aims to assess the diagnostic yield of routine EEG (rEEG) followed by long-term ambulatory EEG (aEEG) in a retrospective cohort, focusing on the rates of abnormal EEG findings, and overall event capture.

METHODS

Data were retrospectively collected from deidentified clinical reports of patients who underwent both rEEG and subsequent aEEG, with both modalities including video recordings. The study included 95 patients, with demographic, clinical information, and EEG findings extracted for analysis. Statistical analyses included chi-squared proportion tests and Wilcoxon rank-sum tests to assess the influence of variables such as age, sex, referral source, and aEEG duration on outcomes. Bayes factors were calculated to evaluate the power of the statistical tests.

RESULTS

Among the 95 patients, 33 % were 16 years old or younger. The median duration of aEEG was 3.9 days. Abnormal EEG findings increased from 18 % with rEEG to 33 % with aEEG. Epileptic seizures were captured in 3 % of rEEG and 8 % of aEEG, while non-epileptic events were captured in 35 % of aEEG compared to none in rEEG. Younger age was associated with higher rates of abnormal findings, but this was not adequately powered. Females had a higher likelihood of event capture on aEEG, though this finding was also underpowered. The majority of adult and paediatric patients with a normal rEEG went on to have a normal aEEG.

CONCLUSION

Ambulatory EEG significantly improves the diagnostic yield for both epileptic and non-epileptic events compared to routine EEG, particularly in adults. This study supports the broader use of aEEG for comprehensive epilepsy evaluation and suggests further research to optimise its clinical utility.

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the EEG 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-Source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

Effect evaluation of outpatient long-term video EEGs for people with seizure disorders - study protocol of the ALVEEG project: a randomized controlled trial in Germany

BACKGROUND

Epilepsy and other seizure disorders account for a high disease burden in Germany. As a timely diagnosis and accurate treatment are crucial, improving the management of these disorders is important. Outside of Germany, outpatient long-term video EEGs (ALVEEGs) have demonstrated the potential to support the diagnosis and management of epilepsy and other seizure disorders. This study aims to evaluate the implementation of ALVEEGs as a new diagnostic pathway in eastern parts of Germany to diagnose epilepsy and other seizure disorders and to assess if ALVEEGs are equally effective as the current inpatient-monitoring gold standard, which is currently only available at a limited number of specialized centers in Germany.

METHODS

ALVEEG is a prospective, multicenter, randomized controlled equivalence trial, involving five epilepsy centers in the eastern states of Germany. Patients will be randomized into either intervention (IG) or control group (CG), using a permuted block randomization. The sample size targeted is 688 patients, continuously recruited over the trial. The IG will complete an ALVEEG in a home setting, including getting access to a smartphone app to document seizure activity. The CG will receive care as usual, i.e., inpatient long-term video-EEG monitoring. The primary outcome is the proportion of clinical questions being solved in the IG compared to the CG. Secondary outcomes include hospital stays, time until video EEG, time until diagnosis and result discussion, patients' health status, quality of life and health competence, and number and form of epilepsy-related events and epileptiform activity. Alongside the trial, a process implementation and health economic evaluation will be conducted.

DISCUSSION

The extensive evaluation of this study, including an implementation and health economic evaluation, will provide valuable information for health policy decision-makers to optimize future delivery of neurological care to patients affected by epilepsy and other seizure disorders and on the uptake of ALVEEG into standard care in Germany.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00032220), date registered: December 11, 2023.

EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility?

Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.

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.

Get Out the Door: Ambulatory EEG Trumps Routine EEG in the Detection of Interictal Epileptiform Abnormalities After a First Unprovoked Seizure

Diagnostic Accuracy of Ambulatory EEG vs Routine EEG in Patients With First Single Unprovoked Seizure Hernandez-Ronquillo L, Thorpe L, Feng C, Hunter G, Dash D, Hussein T, Dolinsky C, Waterhouse K, Roy P, Jette N. Neurol Clin Pract . 2023;13(3). doi:10.1212/CPJ.0000000000200160 Background and Objective: To evaluate the diagnostic accuracy of the ambulatory EEG (aEEG) at detecting interictal epileptiform discharges (IEDs)/seizures compared with routine EEG (rEEG) and repetitive/second rEEG in patients with a first single unprovoked seizure (FSUS). We also evaluated the association between IED/seizures on aEEG and seizure recurrence within 1 year of follow-up. Methods: We prospectively evaluated 100 consecutive patients with FSUS at the provincial Single Seizure Clinic. They underwent 3 sequential EEG modalities: first rEEG, second rEEG, and aEEG. Clinical epilepsy diagnosis was ascertained based on the 2014 International League Against Epilepsy definition by a neurologist/epileptologist at the clinic. An EEG-certified epileptologist/neurologist interpreted all 3 EEGs. All patients were followed up for 52 weeks until they had either second unprovoked seizure or maintained single seizure status. Accuracy measures (sensitivity, specificity, negative and positive predictive values, and likelihood ratios), receiver operating characteristic (ROC) analysis, and area under the curve (AUC) were used to evaluate the diagnostic accuracy of each EEG modality. Life tables and the Cox proportional hazard model were used to estimate the probability and association of seizure recurrence. Results: Ambulatory EEG captured IED/seizures with a sensitivity of 72%, compared with 11% for the first rEEG and 22% for the second rEEG. The diagnostic performance of the aEEG was statistically better (AUC: 0.85) compared with the first rEEG (AUC: 0.56) and second rEEG (AUC: 0.60). There were no statistically significant differences between the 3 EEG modalities regarding specificity and positive predictive value. Finally, IED/seizure on the aEEG was associated with more than 3 times the hazard of seizure recurrence. Discussion: The overall diagnostic accuracy of aEEG at capturing IED/seizures in people presenting with FSUS was higher than the first and second rEEGs. We also found that IED/seizures on the aEEG were associated with an increased risk of seizure recurrence. Classification of Evidence: This study provides Class I evidence supporting that, in adults with First Single Unprovoked Seizure (FSUS), 24-h ambulatory EEG has increased sensitivity when compared with routine and repeated EEG.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy

OBJECTIVE

To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy.

METHODS

Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication.

RESULTS

Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients.

SIGNIFICANCE

There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

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.

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.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles

This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.

Online Learning for Wearable EEG-Based Emotion Classification

Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9% (Arousal) and 25.8% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87% (Arousal) and 82% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification.

Machine learning seizure prediction: one problematic but accepted practice

Objective.Epilepsy is one of the most common neurological disorders and can have a devastating effect on a person's quality of life. As such, the search for markers which indicate an upcoming seizure is a critically important area of research which would allow either on-demand treatment or early warning for people suffering with these disorders. There is a growing body of work which uses machine learning methods to detect pre-seizure biomarkers from electroencephalography (EEG), however the high prediction rates published do not translate into the clinical setting. Our objective is to investigate a potential reason for this.Approach.We conduct an empirical study of a commonly used data labelling method for EEG seizure prediction which relies on labelling small windows of EEG data in temporal groups then selecting randomly from those windows to validate results. We investigate a confound for this approach for seizure prediction and demonstrate the ease at which it can be inadvertently learned by a machine learning system.Main results.We find that non-seizure signals can create decision surfaces for machine learning approaches which can result in false high prediction accuracy on validation datasets. We prove this by training an artificial neural network to learn fake seizures (fully decoupled from biology) in real EEG.Significance.The significance of our findings is that many existing works may be reporting results based on this confound and that future work should adhere to stricter requirements in mitigating this confound. The problematic, but commonly accepted approach in the literature for seizure prediction labelling is potentially preventing real advances in developing solutions for these sufferers. By adhering to the guidelines in this paper future work in machine learning seizure prediction is more likely to be clinically relevant.

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.

EEG as an indispensable tool during and after the COVID-19 pandemic: A review of tribulations and successes

During the coronavirus disease 2019 (COVID-19) pandemic, elective and non-emergent tests and procedures were delayed or suspended in lieu of diverting resources to more emergent treatment of critically ill patients and to avoid the spread and contraction of COVID-19. Further, the workforce was stretched thin, and healthcare facilities saw high turnover rates for full-time and contract employees, which strained the system and reduced the ability to provide clinical services. One of the casualties of these changes was electroencephalography (EEG) procedures, which have been performed less frequently throughout the world since the pandemic. Whether considered routine or emergent, the deferral of EEG studies can cause downstream effects, including a delay in diagnosis and initiation of treatment for epilepsy and non-epileptic seizures resulting in a higher risk of morbidity and mortality. Despite these limitations, the importance and utility of EEG and EEG technologists have been reinforced with the development of COVID-related neurological complications, including encephalopathy and seizures, which require EEG for diagnosis and treatment. Since the pandemic, reliance on remote telemonitoring has further highlighted the value and ease of using EEG. There has also been a heightened interest in rapid EEG devices that non-technologist professionals can attach quickly, allowing minimum patient contact to avoid exposure to COVID-19 and taking advantage of remote EEG monitoring. This review discusses the acute and potential long-term effects of the COVID-19 pandemic on the use and performance of EEG.

Clinical utility of a video/audio-based epilepsy monitoring system Nelli

OBJECTIVE

The aim of this study was to evaluate the clinical utility of a semi-automated hybrid video/audio-based epilepsy monitoring system (Nelli®) in a home setting.

METHODS

In this retrospective study, 104 consecutive patients underwent Nelli-registration for an average of 29 days at their home. The seizure-related data obtained from the registration were assessed to investigate the utility of the Nelli-registration regarding clinical decision-making.

RESULTS

Of 104 patients, Nelli® hybrid system was able to recognize clinically relevant events in 83 (80%) patients: epileptic seizures in 67 (65%) and nonepileptic events in 16 (15%). A total of 2767 epileptic seizures of different seizure types were captured and identified. These seizures included not only tonic-clonic seizures but also other complex or simple motor seizures. For the outcomes regarding clinical decision-making, a need for a new therapeutic intervention was recognized in 54 (51.9%) patients based on the number and severity of seizures captured by Nelli-registration. In 12 (11.5%) patients, the need to change the treatment plan was excluded because no evidence of suspected epileptic seizures was found. Nelli-registration aided in confirming the therapeutic efficacy of modifications of antiseizure medications (ASMs) or neuromodulation therapies in 13 (12.5%) patients. Nelli-registration enabled to determine the change in seizure classification and facilitated to reach clear diagnostic conclusions in 11 (10.6%) patients. In 14 (13.5%) patients, there was no change in clinical outcome, as Nelli-registration was unable to infer any clinical decision either due to inconclusive results or lack of typical events. Seizures detected during Nelli-registration aided in decision-making for therapeutic interventions in 71 (68%) patients. Altogether, 44 (42%) patients had adjustment of ASMs, and in 9 (9%) patients, Nelli-registrations led to the change in the settings of vagus nerve stimulation (VNS) or deep brain stimulation (DBS) treatment. Additionally, 18 (17%) patients were referred to presurgical evaluation or established a baseline seizure frequency before surgical implantation for neuromodulation treatment with VNS or DBS, while 33 (32%) patients had no change in therapy. Nine patients (8.7%) were referred to video-EEG monitoring (VEM), as Nelli-recorded events highlighted the need for presurgical evaluation in 6 patients or further diagnostic evaluation in 3 patients.

CONCLUSION

This study confirms the clinical utility of the video/audio monitoring system Nelli® in home settings. Home monitoring with Nelli® hybrid system provides a new alternative for the assessment of frequency and type of epileptic seizures as well as for a recognition of nonepileptic events. Thus, Nelli-registration can facilitate the optimization of seizure monitoring and management in clinical practice, complementing existing methods such as VEM and ambulatory EEG recordings.

A Prospective Randomized Controlled Trial: Alternative Approach to EEG Application to Reduce Electrode-induced Skin Injury among Ambulatory EEG Patients

Ambulatory electroencephalography (AEEG) is a technique of continuous EEG recording of patients in their natural setting, outside the controlled environment of the hospital. Electrode-induced skin injury is a common complication of prolonged EEG monitoring. This randomized study aimed to investigate the performance of two methods of electrode application in reducing electrode-induced skin injury among patients undergoing 4-day AEEG monitoring. A randomized interventional study was conducted from November 2020 to May 2021 in the Neurosciences Ambulatory Care Unit at a metropolitan hospital in Sydney, Australia. We enrolled patients into two groups: i) Group 1 (standard protocol group) received Ten20 Conductive Paste^TM with Tensive® adhesive gel as the primary approach to electrode application and ii) Group 2 (intervention group) received Ten20 Conductive Paste^TM with Tensive® adhesive gel and hydrogel electrodes on hairless locations as the primary approach to electrode application. A total of 79 patients participated in this study. The group that received the addition of hydrogel electrodes (Group 2) performed better than the standard protocol group on electrode site inflammation for the frontal region, particularly FP1, FP2, F8, and the ground electrode sites. EEG quality and self-reports of patient comfort and mood did not differ significantly between the two groups. The addition of hydrogel electrodes using a Ten20 Conductive Paste^TM with a Tensive® adhesive gel protocol results in reduced inflammation at frontal lobe and ground electrode sites.

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Objective

To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.

Methods

A total of 156 cases of secondary epilepsy with lower cavity hemorrhage in our hospital were selected and divided into the late epilepsy group and the early epilepsy group according to seizure time, and the nonseizure group and the seizure group according to seizure condition. General data of patients were collected, the EEG types of each group were analyzed, and the disease recurrence rate, treatment effect, and symptom onset time were compared.

Results

Rapid and slow and rapid blood flow velocity were the main abnormal manifestations of epilepsy secondary to inferior cavity hemorrhage, accounting for 33.3% and 18.6%, respectively. Compared with the seizure group, the proportion of type ii and type iii in the nonseizure group was higher, and the proportion of type ii and type iii in the early epilepsy group was higher than in the late epilepsy group (P < 0.05). The diagnostic accuracy, missed diagnosis rate, misdiagnosis rate, specificity, and sensitivity of the EEG wave pattern were 94.9%, 3.2%, 1.9%, 91.7%, and 96.2%, respectively. Compared with the early epilepsy group, the recurrence rate of type iii and type iv in the late epilepsy group was higher (P < 0.05). The effective rates of the attack group and the nonattack group were 72.7% and 97.0%, respectively. Compared with the attack group, the effective rate of the nonattack group was higher (P < 0.05). The effective rates of the early epilepsy group and the late epilepsy group were 91.7% and 85.0%, respectively. Compared with the late epilepsy group, the effective rate of the early epilepsy group was higher (P < 0.05). Compared with the early epilepsy group, the late epilepsy group had longer tonic-clonic seizures, atonic seizures, and absent seizures, and the difference between the groups was statistically significant (P < 0.05).

Conclusion

In aSAH secondary epilepsy disease prediction, based on indepth study of the scalp EEG wave type prediction, they play an important role, including aSAH high-risk secondary epilepsy wave types for V, III, and IV types, as well as early and late epilepsy associated with disease stage. Through the diagnosis method to predict the severity of disease, this builds a good foundation for clinical treatment. It is beneficial to improve the effective rate of treatment.

Remote Patient Monitoring for Neuropsychiatric Disorders: A Scoping Review of Current Trends and Future Perspectives from Recent Publications and Upcoming Clinical Trials

Introduction:

Telemedicine and remote patient monitoring are rapidly growing fields. This scoping review provides an update on remote patient monitoring for neuropsychiatric disorders from recent publications and upcoming clinical trials.

Methods:

Publications (PubMed and ICHUSHI; published January 2010 to February 2021) and trials (ClinicalTrials.gov and Japanese registries; active or recruiting by March 2021) that assessed wearable devices for remote management and/or monitoring of patients with neuropsychiatric disorders were searched. The review focuses on disorders with ≥3 publications.

Results:

We identified 44 publications and 51 active or recruiting trials, mostly from 2019 or 2020. Research on digital devices was most common for Parkinson's disease (11 publications and 19 trials), primarily for monitoring motor symptoms and/or preventing falls. Other disorders (3–5 publications each) included epilepsy (electroencephalogram [EEG] and seizure prediction), sleep disorder (sleep outcomes and behavioral therapies), multiple sclerosis (physical activity and symptoms), depression (physical activity, symptoms, and behavioral therapies), and amyotrophic lateral sclerosis (symptoms). Very few studies focused on newly emerging technologies (e.g., in-ear EEG and portable oximeters), and few studies integrated remote symptom monitoring with telemedicine.

Discussion:

Currently, development of digital devices for daily symptom monitoring is focused on Parkinson's disease. For the diseases reviewed, studies mostly focused on physical activity rather than psychiatric or nonmotor symptoms. Although the validity and usefulness of many devices are established, models for implementing remote patient monitoring in telehealth settings have not been established.

Conclusions:

Verification of the clinical effectiveness of digital devices combined with telemedicine is needed to further advance remote patient care for neuropsychiatric disorders.

Current Status and Future Objectives of Surgical Therapies for Epilepsy in Japan

This study investigated the number of epilepsy surgeries performed over time in Japan, and conducted a questionnaire survey of the Japan Neurosurgical Society (JNS) training program core hospitals to determine the current status and future objectives of surgical therapies and epilepsy training programs for physicians in Japan. This article presents part of a presentation delivered as a presidential address at the 44th Annual Meeting of the Epilepsy Surgery Society of Japan held in January 2021. The number of epilepsy surgeries performed per year has increased in Japan since 2011 to around 1,200 annually between 2015 and 2018. The questionnaire survey showed that 50% of the responding hospitals performed epilepsy surgery and 29% had an epilepsy center, and that these hospitals provided senior residents with education regarding epilepsy surgery. The presence of an epilepsy center in a hospital was positively correlated with the availability of long-term video electroencephalography monitoring beds as well as the number of epilepsy surgeries performed at the hospital. In regions with no medical facilities offering specialized surgical therapies for epilepsy, the JNS training program core hospitals may help improve epilepsy diagnosis and treatment. They may also increase the number of safe and effective surgeries by establishing epilepsy centers that can perform long-term video electroencephalography monitoring, providing junior neurosurgeons with training regarding epilepsy, and playing a core role in surgical therapies for epilepsy in tertiary medical areas in close cooperation with neighboring medical facilities.

When should we obtain a routine EEG while managing people with epilepsy?

More than eight decades after its discovery, routine electroencephalogram (EEG) remains a safe, noninvasive, inexpensive, bedside test of neurological function. Knowing when a routine EEG should be obtained while managing people with epilepsy is a critical aspect of optimal care. Despite advances in neuroimaging techniques that aid diagnosis of structural lesions in the central nervous system, EEG continues to provide critical diagnostic evidence with implications on treatment. A routine EEG performed after a first unprovoked seizure can support a clinical diagnosis of epilepsy and differentiate those without epilepsy, classify an epilepsy syndrome to impart prognosis, and characterize seizures for antiseizure management. Despite a current viral pandemic, EEG services continue, and the value of routine EEG is unchanged.