Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions.

OBJECTIVE: Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice. METHOD: Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG. RESULTS: Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome. CONCLUSION: Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong. SIGNIFICANCE: These results underline the need for standardization of SEEG terminology.

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence.

Introduction: Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods: In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results: At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion: These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

The online educational tool "Roadmap to EEGs" significantly improved trainee performance in recognizing EEG patterns.

OBJECTIVE: We created a framework to assess the competency-based EEG curriculum, outlined by the International League Against Epilepsy (ILAE) through a video-based online educational resource ("Roadmap to EEGs") and assessed its effectiveness and feasibility in improving trainees' knowledge. METHODS: Ten video-based e-learning modules addressed seven key topics in EEG and epileptology (normal EEG, normal variants, EEG artifacts, interictal epileptiform discharges (IED), focal seizures, idiopathic generalized epilepsy (IGE), and developmental and epileptic encephalopathies (DEE)). We posted the educational videos on YouTube for free access. Pre- and post-tests, each comprising 20 multiple-choice questions, were distributed to institution leadership and advertised on social media platforms to reach a global audience. The tests were administered online to assess the participants' knowledge. Pre- and post-test questions showed different EEG samples to avoid memorization and immediate recall. After completing the post-test, participants were asked to respond to 7 additional questions assessing their confidence levels and recommendations for improvement. RESULTS: A total of 52 complete and matched pre- and post-test responses were collected. The probability of a correct response was 73% before teaching (95% CI: 70%-77%) and 81% after teaching (95% CI: 78%-84%). The odds of a correct response increased significantly by 59% (95% CI: 28%-98%, p < .001). For participants having >4 weeks of EEG training, the probability of a correct response was 76% (95% CI: .72-.79) and 81% after teaching (95% CI: .78-.84). The odds of answering correctly increased by 44% (95% CI: 15%-80%, p = .001). Participants felt completely confident in independently interpreting and identifying EEG findings after completing the teaching modules (17.1% before vs. 37.8% after, p-value < .0001). 86.5% of participants expressed a high likelihood of recommending the module to other trainees. SIGNIFICANCE: The video-based online educational resource allows participants to acquire foundational knowledge in EEG/epilepsy, and participants to review previously learned EEG/epilepsy information.

Seizure provocation in EEG recordings: A data-driven approach.

OBJECTIVE: Recording seizures on video-EEG has a high diagnostic value. However, bilateral convulsive seizures constitute a risk for the patients. Our aim was to investigate the diagnostic yield and associated risks of provocation methods in short-term video-EEGs. METHODS: We extracted data on seizures and provocation methods from a large database of short-term video-EEGs with standardized annotations using SCORE (Standardized Computer-based Organized reporting of EEG). RESULTS: 2742 paroxysmal clinical episodes were recorded in 11 919 consecutive EEGs. Most epileptic seizures (54%) were provoked. Hyperventilation provoked most of typical absence seizures (55%), intermittent photic stimulation (IPS) provoked myoclonic seizures (25%) and most of bilateral convulsive seizures (55%), while 43% of focal seizures were precipitated by sleep. All but one of the 16 bilateral convulsive seizures were provoked by IPS or sleep. Latency between start of generalized photoparoxysmal EEG response and bilateral convulsive seizures were ≤3 s in all but one patient. SIGNIFICANCE: The large, structured database provides evidence for the diagnostic utility of various provocation methods in short-term video-EEGs. The risk of bilateral convulsive seizures is relatively small, but it cannot be prevented by stopping IPS after 3 s. A priori knowledge about seizure semiology helps planning patient-tailored provocation strategy in short-term video-EEGs.

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study.

OBJECTIVE: Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort. METHODS: Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders. RESULTS: The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%). SIGNIFICANCE: The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study.

OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers.

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of [Formula: see text] for 182 seizures from the CHB-MIT dataset and [Formula: see text] for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of [Formula: see text] (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms-up to [Formula: see text] compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

OBJECTIVE: Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. METHODS: We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. RESULTS: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. SIGNIFICANCE: Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

Recent advances in clinical electroencephalography.

PURPOSE OF REVIEW: Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients. RECENT FINDINGS: Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field. SUMMARY: This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom-built wearable device.

OBJECTIVE: To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. METHODS: The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. RESULTS: Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. SIGNIFICANCE: Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.

Prediction tools and risk stratification in epilepsy surgery.

OBJECTIVE: This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2). METHODS: We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures. RESULTS: The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05). SIGNIFICANCE: ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.

Expert accuracy and inter-rater agreement of "must-know" EEG findings for adult and child neurology residents.

OBJECTIVE: We published a list of "must-know" routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter-rater agreement (IRA) of these "must-know" rEEG findings among international experts. METHODS: A previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple-choice questions predicated on the previously published "must-know" rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de-identified 10-20-s epochs of EEG that were considered unequivocal examples by five EEG experts. RESULTS: The examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain-specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p = .035). Country-specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of "must-know" rEEG findings are suboptimal and heterogeneous. SIGNIFICANCE: We recommend the development and implementation of pragmatic, accessible, country-specific ways to measure and improve the expert accuracy and IRA.

On the clinical utility of on-scalp MEG: A modeling study of epileptic activity source estimation.

OBJECTIVE: Epilepsy surgery requires localization of the seizure onset zone (SOZ). Today this can only be achieved by intracranial electroencephalography (iEEG). The iEEG electrode placement is guided by findings from non-invasive modalities that cannot themselves detect SOZ-generated initial seizure activity. On scalp magnetoencephalography (osMEG), with sensors placed on the scalp, demonstrates higher sensitivity than conventional MEG (convMEG) and could potentially detect early seizure activity. Here, we modeled EEG, convMEG and osMEG to compare the modalities' ability to localize SOZ activity and to detect epileptic spikes. METHODS: We modeled seizure propagation within ten epileptic networks located in the mesial and lateral temporal lobe; basal, dorsal, central and frontopolar frontal lobe; parietal and occipital lobe as well as insula and cingulum. The networks included brain regions often involved in focal epilepsy. 128-channel osMEG, convMEG, EEG and combined osMEG + EEG and convMEG + EEG were modeled, and the SOZ source estimation accuracy was quantified and compared using Student's t-test. RESULTS: OsMEG was significantly (p-value <0.01) better than both convMEG and EEG at detecting the earliest SOZ-generated seizure activity and epileptic spikes, and better at localizing seizure activity from all epileptic networks (p < 0.01). CONCLUSIONS: Our modeling results clearly show that osMEG has an unsurpassed potential to detect both epileptic spikes and seizure activity from all simulated anatomical sites. SIGNIFICANCE: No clinically available non-invasive technique can detect SOZ activity from all brain regions. Our study indicates that osMEG has the potential to become an important clinical tool, improving both non-invasive SOZ localization and iEEG electrode placement accuracy.

Artificial intelligence in epilepsy phenotyping.

Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.

Pitfalls in scalp EEG: Current obstacles and future directions.

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives.

INTRODUCTION AND PURPOSE: The continuously expanding research and development of wearable devices for automated seizure detection in epilepsy uses mostly non-invasive technology. Real-time alarms, triggered by seizure detection devices, are needed for safety and prevention to decrease seizure-related morbidity and mortality, as well as objective quantification of seizure frequency and severity. Our review strives to provide a state-of-the-art on automated seizure detection using non-invasive wearable devices in an ambulatory (home) environment and to highlight the prospects for future research. METHODS: A joint working group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) recently published a clinical practice guideline on automated seizure detection using wearable devices. We updated the systematic literature search for the period since the last search by the joint working group. We selected studies qualifying minimally as phase-2 clinical validation trials, in accordance with standards for testing and validation of seizure detection devices. RESULTS: High-level evidence (phases 3 and 4) is available only for the detection of tonic-clonic seizures and major motor seizures when using wearable devices based on accelerometry, surface electromyography (EMG), or a multimodal device combining accelerometry and heart rate. The reported sensitivity of these devices is 79.4-96%, with a false alarm rate of 0.20-1.92 per 24 hours (0-0.03 per night). A single phase-3 study validated the detection of absence seizures using a single-channel wearable EEG device. Two phase-4 studies showed overall user satisfaction with wearable seizure detection devices, which helped decrease injuries related to tonic-clonic seizures. Overall satisfaction, perceived sensitivity, and improvement in quality-of-life were significantly higher for validated devices. CONCLUSIONS: Among the vast number of studies published on seizure detection devices, most are strongly affected by potential bias, providing a too-optimistic perspective. By applying the standards for clinical validation studies, potential bias can be reduced, and the quality of a continuously growing number of studies in this field can be assessed and compared. The ILAE-IFCN clinical practice guideline on automated seizure detection using wearable devices recommends using clinically validated wearable devices for automated detection of tonic-clonic seizures when significant safety concerns exist. The studies published after the guideline was issued only provide incremental knowledge and would not change the current recommendations.

Added value of advanced workup after the first seizure: A 7-year cohort study.

OBJECTIVE: This study was undertaken to establish whether advanced workup including long-term electroencephalography (LT-EEG) and brain magnetic resonance imaging (MRI) provides an additional yield for the diagnosis of new onset epilepsy (NOE) in patients presenting with a first seizure event (FSE). METHODS: In this population-based study, all adult (≥16 years) patients presenting with FSE in the emergency department (ED) between March 1, 2010 and March 1, 2017 were assessed. Patients with obvious nonepileptic or acute symptomatic seizures were excluded. Routine EEG, LT-EEG, brain computed tomography (CT), and brain MRI were performed as part of the initial workup. These examinations' sensitivity and specificity were calculated on the basis of the final diagnosis after 2 years, along with the added value of advanced workup (MRI and LT-EEG) over routine workup (routine EEG and CT). RESULTS: Of the 1010 patients presenting with FSE in the ED, a definite diagnosis of NOE was obtained for 501 patients (49.6%). Sensitivity of LT-EEG was higher than that of routine EEG (54.39% vs. 25.5%, p < .001). Similarly, sensitivity of MRI was higher than that of CT (67.98% vs. 54.72%, p = .009). Brain MRI showed epileptogenic lesions in an additional 32% compared to brain CT. If only MRI and LT-EEG were considered, five would have been incorrectly diagnosed as nonepileptic (5/100, 5%) compared to patients with routine EEG and MRI (25/100, 25%, p = .0001). In patients with all four examinations, advanced workup provided an overall additional yield of 50% compared to routine workup. SIGNIFICANCE: Our results demonstrate the remarkable added value of the advanced workup launched already in the ED for the diagnosis of NOE versus nonepileptic causes of seizure mimickers. Our findings suggest the benefit of first-seizure tracks or even units with overnight EEG, similar to stroke units, activated upon admission in the ED.