Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders.

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

The role of the electroencephalogram (EEG) in determining the aetiology of catatonia: a systematic review and meta-analysis of diagnostic test accuracy.

Background: Catatonia is a psychomotor syndrome that has a wide range of aetiologies. Determining whether catatonia is due to a medical or psychiatric cause is important for directing treatment but is clinically challenging. We aimed to ascertain the performance of the electroencephalogram (EEG) in determining whether catatonia has a medical or psychiatric cause, conventionally defined. Methods: In this systematic review and meta-analysis of diagnostic test accuracy (PROSPERO CRD42021239027), Medline, EMBASE, PsycInfo, and AMED were searched from inception to May 11, 2022 for articles published in peer-reviewed journals that reported EEG findings in catatonia of a medical or psychiatric origin and were reported in English, French, or Italian. Eligible study types were clinical trials, cohort studies, case-control studies, cross-sectional studies, case series, and case reports. The reference standard was the final clinical diagnosis. Data extraction was conducted using individual patient-level data, where available, by two authors. We prespecified two types of studies to overcome the limitations anticipated in the data: larger studies (n ≥ 5), which were suitable for formal meta-analytic methods but generally lacked detailed information about participants, and smaller studies (n < 5), which were unsuitable for formal meta-analytic methods but had detailed individual patient level data, enabling additional sensitivity analyses. Risk of bias and applicability were assessed with the QUADAS-2 tool for larger studies, and with a published tool designed for case reports and series for smaller studies. The primary outcomes were sensitivity and specificity, which were derived using a bivariate mixed-effects regression model. Findings: 355 studies were included, spanning 707 patients. Of the 12 larger studies (5 cohort studies and 7 case series), 308 patients were included with a mean age of 48.2 (SD = 8.9) years. 85 (52.8%) were reported as male and 99 had catatonia due to a general medical condition. In the larger studies, we found that an abnormal EEG predicted a medical cause of catatonia with a sensitivity of 0.82 (95% CI 0.67-0.91) and a specificity of 0.66 (95% CI 0.45-0.82) with an I 2 of 74% (95% CI 42-100%). The area under the summary ROC curve offered excellent discrimination (AUC = 0.83). The positive likelihood ratio was 2.4 (95% CI 1.4-4.1) and the negative likelihood ratio was 0.28 (95% CI 0.15-0.51). Only 5 studies had low concerns in terms of risk of bias and applicability, but a sensitivity analysis limited to these studies was similar to the main analysis. Among the 343 smaller studies, 399 patients were included, resulting in a sensitivity of 0.76 (95% CI 0.71-0.81), specificity of 0.67 (0.57-0.76) and AUC = 0.71 (95% CI 0.67-0.76). In multiple sensitivity analyses, the results were robust to the exclusion of reports of studies and individuals considered at high risk of bias. Features of limbic encephalitis, epileptiform discharges, focal abnormality, or status epilepticus were highly specific to medical catatonia, but features of encephalopathy had only moderate specificity and occurred in 23% of the cases of psychiatric catatonia in smaller studies. Interpretation: In cases of diagnostic uncertainty, the EEG should be used alongside other investigations to ascertain whether the underlying cause of catatonia is medical. The main limitation of this review is the differing thresholds for considering an EEG abnormal between studies. Funding: Wellcome Trust, NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust.

The unforeseen future: Impacts of the COVID-19 pandemic on home video-EEG telemetry.

The coronavirus disease 2019 (COVID-19) pandemic had widespread impact on health care systems globally-particularly services arranged around elective admission and attendance such as epilepsy monitoring units and home video-EEG telemetry (HVET). Here, we review the ongoing impacts of the pandemic on HVET services among several different providers who used different initial models of HVET. We discuss the features of HVET that led to success in providing continued diagnostic services to patients with epilepsy and related disorders and through retrospective audit of our services demonstrate the high diagnostic yield of HVET. We reflect on this unforeseen future and its implications for other diagnostic techniques and approaches.

Non-convulsive status epilepticus: COVID-19 or clozapine induced?

We present a case of non-convulsive status epilepticus in a 57-year-old woman with a schizoaffective disorder, without an antecedent seizure history, with two possible aetiologies including SARS-CoV-2 infection and clozapine uptitration. We discuss the presentation, investigations, differential diagnosis and management. In particular, we focus on the electroencephalogram (EEG) findings seen in this case and the electroclinical response to antiepileptic medication. We review the literature and discuss the relevance of this case to the SARS-CoV-2 global pandemic. We emphasise the importance of considering possible neurological manifestations of SARS-CoV-2 infection and highlight seizure disorder as one of the possible presentations. In addition, we discuss the possible effects of clozapine on the electroclinical presentation by way of possible seizure induction as well as discuss the possible EEG changes and we highlight that this needs to be kept in mind especially during rapid titration.

Past, Present and Future of Home video-electroencephalographic telemetry: A review of the development of in-home video-electroencephalographic recordings.

Video-electroencephalographic (EEG) monitoring is an essential tool in epileptology, conventionally carried out in a hospital epilepsy monitoring unit. Due to high costs and long waiting times for hospital admission, coupled with technological advances, several centers have developed and implemented video-EEG monitoring in the patient's home (home video-EEG telemetry [HVET]). Here, we review the history and current status of three general approaches to HVET: (1) supervised HVET, which entails setting up video-EEG in the patient's home with daily visiting technologist support; (2) mobile HVET (also termed ambulatory video-EEG), which entails attaching electrodes in a health care facility, supplying the patient and carers with the hardware and instructions, and then asking the patient and carer to set up recording at home without technologist support; and (3) cloud-based HVET, which adds to either of the previous models continuous streaming of video-EEG from the home to the health care provider, with the option to review data in near real time, troubleshoot hardware remotely, and interact remotely with the patient. Our experience shows that HVET can be highly cost-effective and is well received by patients. We note limitations related to long-term electrode attachment and correct camera placing while the patient is unsupervised at home, and concerns related to regulations regarding data privacy for cloud services. We believe that HVET opens significant new opportunities for research, especially in the field of understanding the many influences in seizure occurrence. We speculate that in the future HVET may merge into innovative new multisensor approaches to continuously monitoring people with epilepsy.

Slower alpha rhythm associates with poorer seizure control in epilepsy.

Objective: Slowing and frontal spread of the alpha rhythm have been reported in multiple epilepsy syndromes. We investigated whether these phenomena are associated with seizure control. Methods: We prospectively acquired resting-state electroencephalogram (EEG) in 63 patients with focal and idiopathic generalized epilepsy (FE and IGE) and 39 age- and gender-matched healthy subjects (HS). Patients were divided into good and poor (≥4 seizures/12 months) seizure control groups based on self-reports and clinical records. We computed spectral power from 20-sec EEG segments during eyes-closed wakefulness, free of interictal abnormalities, and quantified power in high- and low-alpha bands. Analysis of covariance and post hoc t-tests were used to assess group differences in alpha-power shift across all EEG channels. Permutation-based statistics were used to assess the topography of this shift across the whole scalp. Results: Compared to HS, patients showed a statistically significant shift of spectral power from high- to low-alpha frequencies (effect size g = 0.78 [95% confidence interval 0.43, 1.20]). This alpha-power shift was driven by patients with poor seizure control in both FE and IGE (g = 1.14, [0.65, 1.74]), and occurred over midline frontal and bilateral occipital regions. IGE exhibited less alpha power shift compared to FE over bilateral frontal regions (g = -1.16 [-0.68, -1.74]). There was no interaction between syndrome and seizure control. Effects were independent of antiepileptic drug load, time of day, or subgroup definitions. Interpretation: Alpha slowing and anteriorization are a robust finding in patients with epilepsy and might represent a generic indicator of seizure liability.

Home Video Telemetry vs inpatient telemetry: A comparative study looking at video quality.

OBJECTIVE: To compare the quality of home video recording with inpatient telemetry (IPT) to evaluate our current Home Video Telemetry (HVT) practice. METHOD: To assess our HVT practice, a retrospective comparison of the video quality against IPT was conducted with the latter as the gold standard. A pilot study had been conducted in 2008 on 5 patients.Patients (n = 28) were included in each group over a period of one year.The data was collected from referral spreadsheets, King's EPR and telemetry archive.Scoring of the events captured was by consensus using two scorers.The variables compared included: visibility of the body part of interest, visibility of eyes, time of event, illumination, contrast, sound quality and picture clarity when amplified to 200%.Statistical evaluation was carried out using Shapiro-Wilk and Chi-square tests. The P-value of ⩽0.05 was considered statistically significant. RESULTS: Significant differences were demonstrated in lighting and contrast between the two groups (HVT performed better in both).Amplified picture quality was slightly better in the HVT group. CONCLUSION: Video quality of HVT is comparable to IPT, even surpassing IPT in certain aspects such as the level of illumination and contrast. Results were reconfirmed in a larger sample of patients with more variables. SIGNIFICANCE: Despite the user and environmental variability in HVT, it looks promising and can be seriously considered as a preferable alternative for patients who may require investigation at locations remote from an EEG laboratory.

Development, evaluation and implementation of video-EEG telemetry at home.

PURPOSE: To describe the development and implementation of video EEG telemetry (VT) in the patient's home (home video telemetry, HVT) in a single centre. METHODS: HVT met the UK Medical Research Council definition of a complex intervention, and we used its guidance to evaluate the process of piloting, evaluating, developing and implementing this new clinical service. The first phase was a feasibility study, comparing inpatient VT (IVT) with HVT in a test-retest design (n=5), to assess data quality and yield of clinically relevant events. The second phase was a pre-implementation study (n=8), to examine acceptability and satisfaction as well as the costs of IVT and HVT. Subsequently, we implemented the service, and reviewed the outcomes of the first 34 patients. RESULTS: The feasibility study found no difference in the quality of recording or clinical yield between IVT and HVT. The pre-implementation study showed excellent patient satisfaction. We also discuss the findings of the main stakeholder survey (consultants and technicians). Our economic modelling demonstrates a clear financial superiority of HVT over IVT. CONCLUSION: Our findings show that diagnostic HVT for seizure classification and polysomnographies can be carried out safely in the patients' home and poses no security risks for staff. HVT can be effectively integrated into an existing tertiary care service as a routine home or community-based procedure. We hope to encourage other clinical neurophysiology departments and epilepsy centres to take advantage of our experience and consider adopting and implementing HVT, with the aim of a nationwide coverage.

Characteristics of scalp electrical fields associated with deep medial temporal epileptiform discharges.

OBJECTIVE: To determine scalp characteristics of epileptiform discharges arising from medial temporal structures (MT). METHODS: Signal-to-noise ratio was increased by averaging simultaneous recordings from intracranial and scalp electrodes synchronised on discharges recorded by foramen ovale (FO) electrodes. The topography, amplitude and distribution of averaged scalp signals were analysed. RESULTS: Four thousand three hundred and twenty-seven discharges from 20 patients were averaged into 77 patterns. Before averaging, only 9% of discharges were detectable on the scalp without the need of simultaneous FO recordings (SED). A further 72.3% of discharges fell into averaged patterns that could be detected on the scalp as small transients before or after averaging (STBA or STAA). In 18.7% of discharges, no scalp signal was seen after averaging. Whereas most SED patterns had largest amplitude on the scalp at anterior temporal electrodes, STBA and STAA patterns showed greater variability and more widespread scalp fields, suggesting a deeper source. Dipole source localisation modelled the majority of SED patterns as radial dipoles located just behind the eye. In contrast, dipoles corresponding to STBA or STAA patterns showed greater variability in location and orientation and tended to be located at MT. CONCLUSIONS: SED patterns seem to arise from widespread subtemporal and/or superficial neocortical activation, generating EEG fields that are distorted by the high electrical conductivity of anterior cranial foramina. In contrast, STBA and STAA patterns represent electrical fields from neuronal activity more restricted to MT, that reach the scalp highly attenuated by volume-conduction and less distorted by cranial foramina. SIGNIFICANCE: Low amplitude scalp signals can be related to MT activity and must be taken into consideration for the diagnosis of temporal lobe epilepsy, pre-surgical assessment and for valid modelling of deep sources from the scalp EEG and magnetoencephalogram.