Interictal and seizure-onset EEG patterns in malformations of cortical development: A systematic review.

OBJECTIVES: Malformations of cortical development (MCDs) are common causes of drug-resistant epilepsy. The mechanisms underlying the associated epileptogenesis and ictogenesis remain poorly elucidated. EEG can help in understanding these mechanisms. We systematically reviewed studies reporting scalp or intracranial EEG features of MCDs to characterise interictal and seizure-onset EEG patterns across different MCD types. METHODS: We conducted a systematic review in accordance with PRISMA guidelines. MEDLINE, PubMed, and Cochrane databases were searched for studies describing interictal and seizure-onset EEG patterns in MCD patients. A classification framework was implemented to group EEG features into 20 predefined patterns, comprising nine interictal (five, scalp EEG; four, intracranial EEG) and 11 seizure-onset (five, scalp EEG; six, intracranial EEG) patterns. Logistic regression was used to estimate the odds ratios (OR) of each seizure-onset pattern being associated with specific MCD types. RESULTS: Our search yielded 1682 studies, of which 27 comprising 936 MCD patients were included. Of the nine interictal EEG patterns, five (three, scalp EEG; two, intracranial EEG) were detected in ≥2 MCD types, while four (rhythmic epileptiform discharges type 1 and type 2 on scalp EEG; repetitive bursting spikes and sporadic spikes on intracranial EEG) were seen only in focal cortical dysplasia (FCD). Of the 11 seizure-onset patterns, eight (three, scalp EEG; five, intracranial EEG) were found in ≥2 MCD types, whereas three were observed only in FCD (suppression on scalp EEG; delta brush on intracranial EEG) or tuberous sclerosis complex (TSC; focal fast wave on scalp EEG). Among scalp EEG seizure-onset patterns, paroxysmal fast activity (OR = 0.13; 95% CI: 0.03-0.53; p = 0.024) and repetitive epileptiform discharges (OR = 0.18; 95% CI: 0.05-0.61; p = 0.036) were less likely to occur in TSC than FCD. Among intracranial EEG seizure-onset patterns, low-voltage fast activity was more likely to be detected in heterotopia (OR = 19.3; 95% CI: 6.22-60.1; p < 0.001), polymicrogyria (OR = 6.70; 95% CI: 2.25-20.0; p = 0.004) and TSC (OR = 4.27; 95% CI: 1.88-9.70; p = 0.005) than FCD. SIGNIFICANCE: Different MCD types can share similar interictal or seizure-onset EEG patterns, reflecting common underlying biological mechanisms. However, selected EEG patterns appear to point to distinct MCD types, suggesting certain differences in their neuronal networks.

Clinical and EEG factors associated with antiseizure medication resistance in idiopathic generalized epilepsy.

OBJECTIVE: We sought to determine which combination of clinical and electroencephalography (EEG) characteristics differentiate between an antiseizure medication (ASM)-resistant vs ASM-responsive outcome for patients with idiopathic generalized epilepsy (IGE). METHODS: This was a case-control study of ASM-resistant cases and ASM-responsive controls with IGE treated at five epilepsy centers in the United States and Australia between 2002 and 2018. We recorded clinical characteristics and findings from the first available EEG study for each patient. We then compared characteristics of cases vs controls using multivariable logistic regression to develop a predictive model of ASM-resistant IGE. RESULTS: We identified 118 ASM-resistant cases and 114 ASM-responsive controls with IGE. First, we confirmed our recent finding that catamenial epilepsy is associated with ASM-resistant IGE (odds ratio [OR] 3.53, 95% confidence interval [CI] 1.32-10.41, for all study subjects) after covariate adjustment. Other independent factors seen with ASM resistance include certain seizure-type combinations (absence, myoclonic, and generalized tonic-clonic seizures [OR 7.06, 95% CI 2.55-20.96]; absence and generalized tonic-clonic seizures [OR 4.45, 95% CI 1.84-11.34]), as well as EEG markers of increased generalized spike-wave discharges (GSWs) in sleep (OR 3.43, 95% CI 1.12-11.36 for frequent and OR 7.21, 95% CI 1.50-54.07 for abundant discharges in sleep) and the presence of generalized polyspike trains (GPTs; OR 5.49, 95% CI 1.27-38.69). The discriminative ability of our final multivariable model, as measured by area under the receiver-operating characteristic curve, was 0.80. SIGNIFICANCE: Multiple clinical and EEG characteristics independently predict ASM resistance in IGE. To improve understanding of a patient's prognosis, clinicians could consider asking about specific seizure-type combinations and track whether they experience catamenial epilepsy. Obtaining prolonged EEG studies to record the burden of GSWs in sleep and assessing for the presence of GPTs may provide additional predictive value.

Starting a new anti-seizure medication in drug-resistant epilepsy: Add-on or substitute?

OBJECTIVES: Randomized studies in drug-resistant epilepsy (DRE) typically involve addition of a new anti-seizure medication (ASM). However, in clinical practice, if the patient is already taking multiple ASMs, then substitution of one of the current ASMs commonly occurs, despite little evidence supporting this approach. METHODS: Longitudinal prospective study of seizure outcome after commencing a previously untried ASM in patients with DRE. Multivariable time-to-event and logistic regression models were used to evaluate outcomes by whether the new ASM was introduced by addition or substitution. RESULTS: A total of 816 ASM changes in 436 adult patients with DRE between 2010 and 2018 were analyzed. The new ASM was added on 407 (50.1%) occasions and substituted on 409 (49.9%). Mean patient follow-up was 3.2 years. Substitution was more likely if the new ASM was enzyme-inducing or in patients with a greater number of concurrent ASMs. ASM add-on was more likely if a γ-aminobutyric acid (GABA) agonist was introduced or if the patient had previously trialed a higher number of ASMs. The rate of discontinuation due to lack of tolerability was similar between the add-on and substitution groups. No difference between the add-on and substitution ASM introduction strategies was observed for the primary outcome of ≥50% seizure reduction at 12 months. SIGNIFICANCE: Adding or substituting a new ASM in DRE has the same influence on seizure outcomes. The findings confirm that ASM alterations in DRE can be individualized according to concurrent ASM therapy and patient characteristics.

Treatment Outcomes of Newly Diagnosed Epilepsy: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVES: Understanding the multi-faceted treatment outcomes of newly diagnosed epilepsy is critical for developing rational therapeutic strategies. A meta-analysis was conducted to derive pooled estimates of a range of seizure outcomes in children and adults with newly diagnosed epilepsy commenced on antiseizure medication treatment, and to identify factors associated with different outcomes. METHODS: PubMed/EMBASE were screened for eligible articles between 1 January, 1995 and 1 May, 2021 to include unselected cohort studies with a ≥ 12-month follow-up of seizure outcomes. Proportions of patients seizure free at different follow-up timepoints and their characteristics at the study population level were extracted. The patients were group-wise aggregated using a random-effects model. Primary outcomes were proportions of patients with cumulative 1-year seizure freedom (C1YSF), and 1-year and 5-year terminal seizure freedom (T1YSF and T5YSF). Secondary outcomes included the proportions of patients with early sustained seizure freedom, drug-resistant epilepsy and seizure-free off antiseizure medication at the last follow-up (off antiseizure medications). A separate random-effects meta-analysis was performed for nine predictors of importance. RESULTS: In total, 39 cohorts (total n = 21,139) met eligibility criteria. They included 15 predominantly adult cohorts (n = 12,024), 19 children (n = 6569), and 5 of mixed-age groups (n = 2546). The pooled C1YSF was 79% (95% confidence interval [CI] 74-83). T1YSF was 68% (95% CI 63-72) and T5YSF was 69% (95% CI 62-75). Children had higher C1YSF (85% vs 68%, p < 0.001) and T1YSF than adult cohorts (74% vs 61%, p = 0.007). For secondary outcomes, 33% (95% CI 27-39) of patients achieved early sustained seizure freedom, 17% (95% CI 13-21) developed drug resistance, and 39% (95% CI 30-50) were off antiseizure medications at the last follow-up. Studies with a longer follow-up duration correlated with higher C1YSF (p < 0.001) and being off antiseizure medications (p = 0.045). Outcomes were not associated with study design (prospective vs retrospective), cohort size, publication year, or the earliest date of recruitment. Predictors of importance in newly diagnosed epilepsy include etiology, epilepsy type, abnormal diagnostics (neuroimaging, examination, and electroencephalogram findings), number of seizure types, and pre-treatment seizure burden. CONCLUSIONS: Seizure freedom is achieved with currently available antiseizure medications in most patients with newly diagnosed epilepsy, yet this is often not immediate, may not be sustainable, and has not improved over recent decades. Symptomatic etiology, abnormal neuro-diagnostics, and increased pre-treatment seizure burden and seizure types are important predictors for unfavorable outcomes in newly diagnosed epilepsy. The study findings may be used as a quantitative benchmark on the efficacy of future antiseizure medication therapy for this patient population.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.

PURPOSE: Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. METHODS: Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. RESULTS: One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. CONCLUSIONS: Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from scalp electroencephalography (EEG) and establish recommendations for the clinical research community. We conduct a systematic review according to the PRISMA guidelines. We searched for studies published between 2012 and 2022 implementing DL for automating IED detection from scalp EEG in major medical and engineering databases. We highlight trends and formulate recommendations for the research community by analyzing various aspects: data properties, preprocessing methods, DL architectures, evaluation metrics and results, and reproducibility. The search yielded 66 studies, and 23 met our inclusion criteria. There were two main DL networks, convolutional neural networks in 14 studies and long short-term memory networks in three studies. A hybrid approach combining a hidden Markov model with an autoencoder was employed in one study. Graph convolutional network was seen in one study, which considered a montage as a graph. All DL models involved supervised learning. The median number of layers was 9 (IQR: 5-21). The median number of IEDs was 11 631 (IQR: 2663-16 402). Only six studies acquired data from multiple clinical centers. AUC was the most reported metric (median: 0.94; IQR: 0.94-0.96). The application of DL to IED detection is still limited and lacks standardization in data collection, multi-center testing, and reporting of clinically relevant metrics (i.e. F1, AUCPR, and false-positive/minute). However, the performance is promising, suggesting that DL might be a helpful approach. Further testing on multiple datasets from different clinical centers is required to confirm the generalizability of these methods.

Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.

The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.

Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis.

Background: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73). Interpretation: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding: MING fonds.

Pharmacoresistance - Epidemiology, mechanisms, and impact on epilepsy treatment.

Understanding the natural history of and factors associated with pharmacoresistant epilepsy provides the foundation for formulating mechanistic hypotheses that can be evaluated to drive the development of novel treatments. This article reviews the modern definition of drug-resistant epilepsy, its prevalence and incidence, risk factors, hypothesized mechanisms, and the implication of recognizing pharmacoresistance in therapeutic strategies. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Primary lateral sclerosis-like picture in a patient with a remote history of anti-N-methyl-D- aspartate receptor (anti-NMDAR) antibody encephalitis.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a well-recognised disorder, first fully characterised in 2007. The long-term sequelae reported thus far include relapses with typical, as well as partial aspects of the well-defined neuropsychiatric syndrome. Rarely, isolated atypical symptoms (diplopia, ataxia and tremor) have been reported as relapse phenomenon. We report a case of a patient with a remote history of likely anti-NMDAR encephalitis with the longest follow-up reported in the literature to date (22 years). The relapse presentation was of a purely upper motor neuron syndrome with a primary lateral sclerosis-like picture.