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This article discusses a case study of a 50-year-old patient who developed symptoms of vitamin B12-responsive methylmalonic acidemia (MMA), a metabolic disorder, at an unusually advanced age. The patient experienced seizures, status epilepticus, leukoencephalopathy, and myelopathy. The article emphasizes the importance of considering inborn metabolic disorders as potential causes of neurological symptoms in adults, even if they present at an older age. The authors recommend conducting biochemical screening investigations and genetic testing to accurately diagnose these conditions. [Extracted from the article]

• Analysis of initial seizure activity using combined high-density EEG/MEG and novel on-scalp MEG (osMEG). • Our study demonstrates that osMEG exhibits a unique ability to detect seizure onset zones non-invasively. • Indicate that osMEG might improve intracranial EEG planning and epilepsy surgery results. 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. 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. 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). Our modeling results clearly show that osMEG has an unsurpassed potential to detect both epileptic spikes and seizure activity from all simulated anatomical sites. 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. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) technology is revolutionizing epilepsy care and management by providing personalized, accessible, and effective approaches to monitoring and treating the condition. This special issue of Epilepsia highlights key advancements in mHealth for epilepsy, including home video-EEG monitoring, wearable devices for automated seizure detection, and seizure forecasting. These developments have been accelerated by the COVID-19 pandemic and the acceptance of telemedicine. The use of mHealth devices has shown high patient satisfaction, improved accuracy of seizure diaries, and enhanced quality of life for individuals with epilepsy. These advancements have the potential to improve treatment strategies and deepen our understanding of the condition. [Extracted from the article]

Objective: To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision‐making. Methods: Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video‐EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web‐based algorithm in their clinical setting. Results: A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%–87.8%), with an agreement coefficient (AC1) of.82 (95% confidence interval =.77–.87), indicating almost perfect agreement. Thirty‐two health care professionals from 14 countries evaluated the feasibility of the web‐based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7‐point Likert scale). Significance: The web‐based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults. [ABSTRACT FROM AUTHOR]

• This clinical practice guideline addresses automated seizure detection using wearable devices. • The guideline was developed by a working group of the ILAE and IFCN using the GRADE system. • Wearable devices are effective for accurate detection of generalized tonic-clonic seizures and focal-to-bilateral tonic-clonic seizures. • It is uncertain whether the detection alarms result in meaningful clinical outcomes for patients until further research is completed. • Wearable devices are recommended for detection of tonic-clonic seizures (weak / conditional recommendation). The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non‐EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy. [ABSTRACT FROM AUTHOR]

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic‐clonic seizures (GTCS) and focal‐to‐bilateral tonic‐clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility. [ABSTRACT FROM AUTHOR]

• Scalp EEG is frequently misinterpreted, causing significant harm to patients. • Limited EEG exposure during residency training underlies electrodiagnostic errors. • Use of operational criteria for IED assessment can help to reduce false positives. • Competency-based curricula can help to close gaps in current EEG educational models. • Machine learning approaches may help to improve diagnostic accuracy. 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. [ABSTRACT FROM AUTHOR]

• Real-time alarms for seizure detection are needed for safety and prevention. • Current literature supports wearable devices only for tonic-clonic seizures. • User satisfaction is significantly higher for validated devices. • Further research is needed for other seizure types and seizure quantification. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Objective: Optimal choice of antiseizure medication (ASM) depends on seizure type, syndrome, age, gender, comorbidities and co‐medications. There are no fixed rules on how to weigh these factors; choices are subjective and experience‐driven. We investigated agreement among experts in selecting ASM as monotherapy and used their prevailing choices to validate a web‐based decision‐support application. Methods: Twenty‐four international experts, blinded to the app, selected the optimal ASM for 25 individual patient‐cases covering a wide variation of seizure types and other factors influencing ASM selection. The app ranked ASMs in order of likely appropriateness for each case. In a second step, experts rated anonymously the choices of the app. Results: Of the 25 patient‐cases (age 13‐74 years), 13 were female, 18 (72%) had comorbidities, six (24%) were on contraceptives, and 13 (52%) had other co‐medications. The median number of experts who selected the same ASM for a given case was 15 (62.5%) and interquartile range (IQR) 13‐18 (54%‐75%). Gwet's agreement coefficient among experts was 0.38 (95% confidence interval [CI] 0.32‐0.44), corresponding to a "fair" agreement. Agreement between the app and the prevailing expert choice for each case was 0.48 (95% CI 0.29‐0.67), corresponding to a "moderate" beyond chance agreement. The percent agreement between the highest ranked selections of the app and the expert selections was 73% (95% CI 64%‐82%). Ninety‐five percent of the experts considered that no incorrect or potentially harmful ASMs were ranked highest by the app, and most experts strongly agreed with the app's selections. Significance: This app, now validated by experts, provides an objective, reproducible method for selecting ASM that accounts for relevant clinical features. It is freely available at: https://epipick.org. [ABSTRACT FROM AUTHOR]

Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high‐risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic‐clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy. [ABSTRACT FROM AUTHOR]

Our goal was to assess the interrater agreement (IRA) of photoparoxysmal response (PPR) using the classification proposed by a task force of the International League Against Epilepsy (ILAE), and a simplified classification system proposed by our group. In addition, we evaluated IRA of epileptiform discharges (EDs) and the diagnostic significance of the electroencephalographic (EEG) abnormalities. We used EEG recordings from the European Reference Network (EpiCARE) and Standardized Computer‐based Organized Reporting of EEG (SCORE). Six raters independently scored EEG recordings from 30 patients. We calculated the agreement coefficient (AC) for each feature. IRA of PPR using the classification proposed by the ILAE task force was only fair (AC = 0.38). This improved to a moderate agreement by using the simplified classification (AC = 0.56; P =.004). IRA of EDs was almost perfect (AC = 0.98), and IRA of scoring the diagnostic significance was moderate (AC = 0.51). Our results suggest that the simplified classification of the PPR is suitable for implementation in clinical practice. [ABSTRACT FROM AUTHOR]

Objective: Antiseizure medications (ASMs) are the first‐line treatment for epilepsy. Many ASMs are available; this offers the opportunity to improve therapy by tailoring it to individual characteristics, but also increases the possibility of healthcare professionals making inappropriate treatment choices. To assist healthcare professionals, we developed a pragmatic algorithm aimed at facilitating medication selection for individuals whose epilepsy begins at age 10 years and older. Methods: Utilizing available evidence and a Delphi panel−based consensus process, a group of epilepsy experts developed an algorithm for selection of ASMs, depending on the seizure type(s) and the presence of relevant clinical variables (age, gender, comorbidities, and comedications). The algorithm was implemented into a web‐based application that was tested and improved in an iterative process. Results: The algorithm categorizes ASMs deemed to be appropriate for each seizure type or combination of seizure types into three groups, with group 1 ASMs considered preferred, group 2 considered second line, and group 3 considered third line. Depending on the presence of relevant clinical variables, the ranking of individual ASMs is adjusted in the prioritization scheme to tailor recommendations to the characteristics of the individual. The algorithm is available on a web‐based application at: https://epipick.org/#/. Significance: The proposed algorithm is user‐friendly, requires less than 2 minutes to complete, and provides the user with a range of appropriate treatment options from which to choose. This should facilitate its broad utilization and contribute to improve epilepsy management for healthcare providers who desire advice, particularly those who lack special expertise in the field. [ABSTRACT FROM AUTHOR]

• The COVID-19 pandemic has led to lockdown of many Epilepsy Monitoring Units. • Postponing video-EEG monitoring of patients had detrimental effects. • Access to video-EEG monitoring be given high priority. Restructuring of healthcare services during the COVID-19 pandemic has led to lockdown of Epilepsy Monitoring Units (EMUs) in many hospitals. The ad-hoc taskforce of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) highlights the detrimental effect of postponing video-EEG monitoring of patients with epilepsy and other paroxysmal events. The taskforce calls for action to continue functioning of Epilepsy Monitoring Units during emergency situations, such as the COVID-19 pandemic. Long-term video-EEG monitoring is an essential diagnostic service. Access to video-EEG monitoring of the patients in the EMUs must be given high priority. Patients should be screened for COVID-19, before admission, according to the local regulations. Local policies for COVID-19 infection control should be adhered to during the video-EEG monitoring. In cases of differential diagnosis where reduction of antiseizure medication is not required, consider home video-EEG monitoring as an alternative in selected patients. [ABSTRACT FROM AUTHOR]

Summary: To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0‐4). We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices. [ABSTRACT FROM AUTHOR]

Summary: Bilateral (generalized) tonic–clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low‐ and high‐frequency signal components. Algorithms targeting increase in high‐frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large‐scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%‐100%). The rate of false alarms (0.7‐2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG‐based seizure detection. [ABSTRACT FROM AUTHOR]

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE are used to report the features of clinical relevance, extracted while assessing the EEGs. Selection of the terms is context sensitive: initial choices determine the subsequently presented sets of additional choices. This process automatically generates a report and feeds these features into a database. In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE is a useful clinical tool, with potential impact on clinical care, quality assurance, data-sharing, research and education. [ABSTRACT FROM AUTHOR]

Objective To elucidate the possible additional diagnostic yield of MEG in the workup of patients with suspected epilepsy, where repeated EEGs, including sleep-recordings failed to identify abnormalities. Methods Fifty-two consecutive patients with clinical suspicion of epilepsy and at least three normal EEGs, including sleep-EEG, were prospectively analyzed. The reference standard was inferred from the diagnosis obtained from the medical charts, after at least one-year follow-up. MEG (306-channel, whole-head) and simultaneous EEG (MEG–EEG) was recorded for one hour. The added sensitivity of MEG was calculated from the cases where abnormalities were seen in MEG but not EEG. Results Twenty-two patients had the diagnosis epilepsy according to the reference standard. MEG–EEG detected abnormalities, and supported the diagnosis in nine of the 22 patients with the diagnosis epilepsy at one-year follow-up. Sensitivity of MEG–EEG was 41%. The added sensitivity of MEG was 18%. MEG–EEG was normal in 28 of the 30 patients categorized as ‘not epilepsy’ at one year follow-up, yielding a specificity of 93%. Conclusions MEG provides additional diagnostic information in patients suspected for epilepsy, where repeated EEG recordings fail to demonstrate abnormality. Significance MEG should be included in the diagnostic workup of patients where the conventional, widely available methods are unrevealing. [ABSTRACT FROM AUTHOR]

There is currently no international consensus procedure for performing comprehensive periictal testing of patients in the epilepsy monitoring units ( EMUs). Our primary goal was to develop a standardized procedure for managing and testing patients during and after seizures in EMUs. The secondary goal was to assess whether it could be implemented in clinical practice (feasibility). A taskforce was appointed by the International League Against Epilepsy ( ILAE)-Commission on European Affairs and the European Epilepsy Monitoring Unit Association, to develop a standardized ictal testing battery ( ITB) based on expert opinion and experience with various local testing protocols. ITB contains a comprehensive set of 10 items that evidence the clinically relevant semiologic features, and it is adaptive to the dynamics of the individual seizures. The feasibility of the ITB was prospectively evaluated on 250 seizures from 152 consecutive patients in 10 centers. ITB was successfully implemented in clinical practice in all 10 participating centers and was considered feasible in 93% of the tested seizures. ITB was not feasible for testing seizures of very short duration. [ABSTRACT FROM AUTHOR]

Muscle activity during seizures is in electroencephalographical (EEG) praxis often considered an irritating artefact. This article discusses ways by surface electromyography (EMG) to turn it into a valuable tool of epileptology. Muscles are in direct synaptic contact with motor neurons. Therefore, EMG signals provide direct information about the electric activity in the motor cortex. Qualitative analysis of EMG has traditionally been a part of the long-term video-EEG recordings. Recent development in quantitative analysis of EMG signals yielded valuable information on the pathomechanisms of convulsive seizures, demonstrating that it was different from maximal voluntary contraction, and different from convulsive psychogenic non-epileptic seizures. Furthermore, the tonic phase of the generalised tonic–clonic seizures (GTCS) proved to have different quantitative features than tonic seizures. The high temporal resolution of EMG allowed detailed characterisation of temporal dynamics of the GTCS, suggesting that the same inhibitory mechanisms that try to prevent the build-up of the seizure activity, contribute to ending the seizure. These findings have clinical implications: the quantitative EMG features provided the pathophysiologic substrate for developing neurophysiologic biomarkers that accurately identify GTCS. This proved to be efficient both for seizure detection and for objective, automated distinction between convulsive and non-convulsive epileptic seizures. [ABSTRACT FROM AUTHOR]

GR reports speaker fees from Eisai, Arvelle, Biocodex, Saniona, and UCB Pharma outside the submitted work. SB reports personal fees from Brain Sentinel, Philips, Epihunter, UCB Pharma, GW Pharma, and Eisai outside the submitted work. [Extracted from the article]

Objective Reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals. A novel method of reconstructing the MEG signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts. Our objective was to evaluate the accuracy of reviewing MEG in source-space. Methods In 60 consecutive patients with epilepsy, we prospectively evaluated the accuracy of reviewing the MEG signals in source-space as compared to the classical method of reviewing them in sensor-space. Results All 46 spike-clusters identified in sensor-space were also identified in source-space. Two additional spike-clusters were identified in source-space. As 29 source-channels can be easily displayed simultaneously, MEG recordings had to be browsed only once. Yet, this yielded a global coverage of the recorded signals and enhanced detectability of epileptiform discharges because MKG-artefacts were suppressed and did not impede evaluation in source-space. Conclusions Our results show that reviewing MEG recordings in source-space is accurate and much more rapid than the classical method of reviewing in sensor-space. Significance This novel method facilitates the clinical use of MEG. [ABSTRACT FROM AUTHOR]

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures. Ann Neurol 2015;77:348-351. [ABSTRACT FROM AUTHOR]

The article offers comments of people on the creation of an international epilepsy day to raise awareness about epilepsy, in response to a letter by doctor M.A. Aleem published in this journal. It mentions the European Epilepsy Day organized by the Commission on European Affairs of the International League Against Epilepsy since 2011, the project Global Campaign Against Epilepsy which aims to improve global awareness, and the support for the day from the tuberous sclerosis complex community.

Objective To investigate the characteristics of sustained muscle activation during convulsive epileptic and psychogenic nonepileptic seizures ( PNES), as compared to voluntary muscle activation. The main goal was to find surface electromyography ( EMG) features that can distinguish between convulsive epileptic seizures and convulsive PNES. Methods In this case-control study, surface EMG was recorded from the deltoid muscles during long-term video-electroencephalography ( EEG) monitoring in 25 patients and in 21 healthy controls. A total of 46 clinical episodes were recorded: 28 generalized tonic-clonic seizures ( GTCS) from 14 patients with epilepsy, and 18 convulsive PNES from 12 patients (one patient had both GTCS and PNES). The healthy controls were simulating GTCS. To quantitatively characterize the signals we calculated the following parameters: root mean square ( RMS) of the amplitude, median frequency ( MF), coherence, and duration of the seizures, of the clonic EMG discharges, and of the silent periods between the cloni. Based on wavelet analysis, we distinguished between a low-frequency component ( LF 2-8 Hz) and a high-frequency component ( HF 64-256 Hz). Results Duration of the seizure, and separation between the tonic and the clonic phases distinguished at group-level but not at individual level between convulsive PNES and GTCS. RMS, temporal dynamics of the HF/ LF ratio, and the evolution of the silent periods differentiated between epileptic and nonepileptic convulsive seizures at the individual level. A combination between HF/ LF ratio and RMS separated all PNES from the GTCS. A blinded review of the EMG features distinguished correctly between GTCS and convulsive PNES in all cases. The HF/ LF ratio and the RMS of the PNES were smaller compared to the simulated seizures. Significance In addition to providing insight into the mechanism of muscle activation during convulsive PNES, these results have diagnostic significance, at the individual level. Surface EMG features can accurately distinguish convulsive epileptic from nonepileptic psychogenic seizures, even in PNES cases without rhythmic clonic movements. A PowerPoint slide summarizing this article is available for download in the Supporting Information section . [ABSTRACT FROM AUTHOR]

Purpose Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography ( EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model. Methods Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy ( STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average ( LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision. Key Findings Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval ( CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones. Significance Source localization of rhythmic ictal activity using a distributed source model ( LAURA) for the ictal EEG signals selected with a standardized method is feasible in clinical practice and has a good diagnostic accuracy. Our findings encourage clinical neurophysiologists assessing ictal EEGs to include this method in their armamentarium. [ABSTRACT FROM AUTHOR]

Keywords: epilepsy; mobile health; seizure detection; seizure prediction; wearable devices EN epilepsy mobile health seizure detection seizure prediction wearable devices S1 S2 2 12/17/20 20201102 NES 201102 Automated seizure detection in ambulatory patients has become a clinical reality, with validated wearable devices offering acceptable performance for detecting generalized tonic-clonic seizures (GTCS) already on the market. The quest for seizure-detection devices and algorithms that would capture the various forms of focal seizures remains a challenge. Epilepsy, mobile health, seizure detection, seizure prediction, wearable devices. [Extracted from the article]

• People with epilepsy have higher risk of poor-quality sleep and of sleep disorders. • Sleep habits and hygiene should be routinely evaluated in clinical practice. • Anti-seizure treatments should be optimized to improve sleep and avoid daytime sedation. • Suspected comorbid sleep disorders should be considered and treated. Poor sleep and daytime sleepiness are common in people with epilepsy. Sleep disorders can disrupt seizure control and in turn sleep and vigilance problems can be exacerbated by seizures and by antiepileptic treatments. Nevertheless, these aspects are frequently overlooked in clinical practice and a clear agreement on the evidence-based guidelines for managing common sleep disorders in people with epilepsy is lacking. Recently, recommendations to standardize the diagnostic pathway for evaluating patients with sleep-related epilepsies and comorbid sleep disorders have been presented. To build on these, we adopted the Delphi method to establish a consensus within a group of experts and we provide practical recommendations for identifying and managing poor night-time sleep and daytime sleepiness in people with epilepsy. We recommend that a comprehensive clinical history of sleep habits and sleep hygiene should be always obtained from all people with epilepsy and their bed partners. A psychoeducational approach to inform patients about habits or practices that may negatively influence their sleep or their vigilance levels should be used, and strategies for avoiding these should be applied. In case of a suspected comorbid sleep disorder an appropriate diagnostic investigation should be performed. Moreover, the possible presence of sleep fragmentation induced by sleep-related seizures should be ruled out. Finally, the dose and timing of antiepileptic medications and other co-medications should be optimized to improve nocturnal sleep and avoid daytime sedation. [ABSTRACT FROM AUTHOR]

The electroencephalography ( EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video- EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan- European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG ( SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, 'episodes' (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists. [ABSTRACT FROM AUTHOR]

Our objective was to assess the clinical reliability of a wrist-worn, wireless accelerometer sensor for detecting generalized tonic-clonic seizures ( GTCS). Seventy-three consecutive patients (age 6-68 years; median 37 years) at risk of having GTCS and who were admitted to the long-term video-electroencephalography ( EEG) monitoring unit ( LTM) were recruited in three centers. The reference standard was considered the seizure time points identified by experienced clinical neurophysiologists, based on the video- EEG recordings and blinded to the accelerometer sensor data. Seizure time points detected real-time by the sensor were compared with the reference standard. Patients were monitored for 17-171 h (mean 66.8; total 4,878). Thirty-nine GTCS were recorded in 20 patients. The device detected 35 seizures (89.7%). In 16 patients all seizures were detected. In three patients more than two thirds of the seizures were detected. The mean of the sensitivity calculated for each patient was 91%. The mean detection latency measured from the start of the focal seizure preceding the secondarily GTCS was 55 s (95% confidence interval [ CI] 38-73 s). The rate of false alarms was 0.2/day. Our results suggest that the wireless wrist accelerometer sensor detects GTCS with high sensitivity and specificity. Patients with GTCS have an increased risk for injuries related to seizures and for sudden unexpected death in epilepsy ( SUDEP), and many nocturnal seizures remain undetected in unattended patients. A portable automatic seizure detection device will be an important tool for helping these patients. [ABSTRACT FROM AUTHOR]

Abstract: The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi-modal detection system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data. [Copyright &y& Elsevier]

Purpose: Previous studies have suggested that cognitive tasks modulate (provoke or inhibit) the epileptiform electroencephalography (EEG) discharges (EDs) in patients with juvenile myoclonic epilepsy (JME). Their inhibitory effect was found to be especially frequent (64-90%). These studies arbitrarily defined modulation as a >100% increase or >50% decrease of the EDs compared with baseline, which may not sufficiently distinguish from spontaneous fluctuations. The aim of our study was to assess the modulation of EDs and the precipitation of myoclonic seizures by cognitive tasks and by conventional provocation methods, taking into account also the spontaneous fluctuation of EDs. Method: Sixty patients with JME underwent video-EEG recordings including 50-min baseline, sleep, hyperventilation, intermittent photic stimulation (IPS), and cognitive tasks. To account for spontaneous fluctuations of the EDs we divided the baseline period into 5-min epochs and calculated the 95% confidence interval for the baseline EDs in each patient. Modulation was assumed when the number of EDs during any 5-min test period was outside the 95% confidence interval. Key Findings: Using the arbitrary method, our results were similar to previous publications: Cognitive tasks seemed to inhibit the EDs in 94% of the patients, and to provoke them in 22%. However, when the spontaneous fluctuations were accounted for, inhibition was found in only 29% of the patients and provocation in 18%. A nonspecific effect of any cognitive task seemed to account for the observed significant inhibition in two-thirds of the cases, but was observed in only one of the patients with significant provocation. Photoparoxysmal response was observed in 23% of the patients. When accounting for the spontaneous occurrence of EDs, IPS had provocative effect in 10% of the patients. Hyperventilation and sleep had provocative effect on EDs to an extent similar to the cognitive tasks (hyperventilation: 22%; sleep: 18%). The conventional provocation methods tended to be more efficient in patients who were not seizure free. Myoclonia were recorded most often during the cognitive tasks (10 patients). Significance: Spontaneous fluctuations of EDs account for most of the previously described inhibitory effect of the cognitive tasks. The provocative effect of the cognitive tasks is task-specific, whereas the inhibitory effect seems to be related to cognitive activation in general. [ABSTRACT FROM AUTHOR]

Patients are not able to call for help during a generalized tonic–clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic–clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of \pm 50\,\mu \V . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7  s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation. [ABSTRACT FROM PUBLISHER]

Objective: To investigate whether epileptic seizures could be predicted or detected by means of spectral analysis of heart rate variability (HRV). Methods: Six patients with temporal lobe epilepsy (4 females, 2 males) participated in the prospective pilot study while enrolled for video/EEG monitoring (24 h/day, 2-4 days). ECG was continuously recorded and 30 min seizure-sessions (25-30 min pre-seizure to 30 sec-5 min post-seizure onset) and 30 min non-seizure-sessions (day- and night sessions for each patient, as control) were chosen for further HRV-analysis. Low frequency (LF) (0.04-0.15 Hz), High frequencies (HF) (0.15-0.40 Hz), LF/HF, LF/(LF+HF) and reciprocal HF-power was determined using continuous FFT- spectral analysis of 64 R-R interval windowing with maximum overlapping. Results: Six seizures were recorded and analyzed from three patients (2 females, 1 male). All of the analyzed EEG-correlated seizures showed reciprocal HF-power peaks between 10 sec pre seizure-onset and 24 sec post seizure-onset with peak amplitudes 2.96-93.63 times higher than control maximum peak. For the other parameters we could not find significant difference between seizure and non-seizure sessions. Conclusion: Specifically high reciprocal HF-power peaks suggest suppressed parasympathetic activity just around seizure-onset time. Seizure detection using HRV-analysis seems to be a promising method for non-invasive seizure detection in the early phase of the clinical event (even preceding the onset). [ABSTRACT FROM AUTHOR]

Objective: Diminished suppression of the P50 auditory evoked potential is a widely used sensory gating phenotype in the molecular genetic studies of schizophrenia. The aim of this study was to explore the relationship between this phenotype and neuregulin 1-related intracellular signaling processes. Method: The P50 evoked potential was recorded in 30 first-episode, never-medicated patients with schizophrenia and in 30 healthy comparison volunteers. Neuregulin 1-induced activation of the phosphoinositide 3'-kinase (PI3K )/protein kinase B (AKT)/glycogen synthase kinase-3β system was characterized by the measurement of the phosphorylated AKT to total AKT ratio in peripheral B lymphoblasts. Results: Relative to comparison subjects, patients with first-episode schizophrenia displayed diminished P50 suppression and decreased neuregulin 1-induced AKT phosphorylation. There was a significant relationship between P50 suppression and AKT phosphorylation. Conclusions: Decreased neuregulin 1-induced activation of the PI3K/AKT system is associated with impaired sensory gating in first-episode schizophrenia. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of the study was to determine the effect of one night''s sleep deprivation on the early and middle-latency median nerve (MN) somatosensory evoked potentials (SEPs). In 20 healthy volunteers, SEPs in response to electrical stimulation of the MN at the wrist were recorded for the 100-ms post-stimulus period, before and after one night of sleep deprivation. The P14 latency was significantly prolonged after sleep deprivation. We found significant increases in the amplitudes of the early parietal (N20–P24) and the frontal middle-latency (P45–N60) components following sleep deprivation. Our results indicate that somatosensory processing is altered after sleep deprivation. [Copyright &y& Elsevier]

We sought to investigate electroencephalographers' real-world behaviors and opinions concerning reading routine EEG (rEEG) with or without clinical information. An eight-question, anonymous, online survey targeted at electroencephalographers was disseminated on social media from the authors' personal accounts and emailed to authors' select colleagues. A total of 389 responses were included. Most respondents reported examining clinical information before describing rEEG findings. Nonetheless, only a minority of respondents believe that EEG analysis/description should be influenced by clinical information. We recommend reviewing clinical data only after an unbiased EEG read to prevent history bias and ensure generation of reliable electrodiagnostic information. [ABSTRACT FROM AUTHOR]

• Analysis of focal epilepsy low-frequency oscillations prior to interictal epileptiform discharges (IEDs) using MEG. • Our study demonstrates consistent low-frequency oscillations prior to IED onset. • These results indicate that IEDs arise from a focal cortical inhibition. Interictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG). Low-frequency (1–8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7–46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation. All patients exhibited statistically significant increase in delta (1–4 Hz) and/or theta (4–8 Hz) oscillations pre-IED compared to baseline [-2000 ms, −1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset. We demonstrated consistently occurring, low-frequency oscillations prior to IED onset. As low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset. [ABSTRACT FROM AUTHOR]

Transient suppression of the motor cortex and of the speech areas cause well-described postictal phenomena following seizures involving the respective cortical areas. Pain is a rare symptom in epileptic seizures. We present a patient with painful tonic seizures in the left leg. The amplitude of the cortical component of the somatosensory evoked potential following stimulation of the left tibial nerve was reduced immediately after the seizure. Our findings suggest that the excitability of the sensory cortex is transiently reduced following a seizure involving the somatosensory area. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including epilepsy, brain disease and wireless communication systems in medical care.

Summary: Objective: Imaging activated glutamate N‐methyl‐D‐aspartate receptor ion channels (NMDAR‐ICs) using positron emission tomography (PET) has proved challenging due to low brain uptake, poor affinity and selectivity, and high metabolism and dissociation rates of candidate radioligands. The radioligand [18F]GE‐179 is a known use‐dependent marker of NMDAR‐ICs. We studied whether interictal [18F]GE‐179 PET would detect foci of abnormal NMDAR‐IC activation in patients with refractory focal epilepsy. Methods: Ten patients with refractory focal epilepsy and 18 healthy controls had structural magnetic resonance imaging (MRI) followed by a 90‐min dynamic [18F]GE‐179 PET scan with simultaneous electroencephalography (EEG). PET and EEG findings were compared with MRI and previous EEGs. Standard uptake value (SUV) images of [18F]GE‐179 were generated and global gray matter uptake was measured for each individual. To localize focal increases in uptake of [18F]GE‐179, the individual SUV images were interrogated with statistical parametric mapping in comparison to a normal database. Additionally, individual healthy control SUV images were compared with the rest of the control database to determine their prevalence of increased focal [18F]GE‐179 uptake. Results: Interictal [18F]GE‐179 PET detected clusters of significantly increased binding in eight of 10 patients with focal epilepsy but none of the controls. The number of clusters of raised [18F]GE‐179 uptake in the patients with epilepsy exceeded the focal abnormalities revealed by the simultaneously recorded EEG. Patients with extensive clusters of raised [18F]GE‐179 uptake showed the most abnormal EEGs. Significance: Detection of multiple foci of abnormal NMDAR‐IC activation in 80% of our patients with refractory focal epilepsy using interictal [18F]GE‐179 PET could reflect enhanced neuronal excitability due to chronic seizure activity. This indicates that chronic epileptic activity is associated with abnormal NMDAR ion channel activation beyond the initial irritative zones. [18F]GE‐179 PET could be a candidate marker for identifying pathological brain areas in patients with treatment‐resistant focal epilepsy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "The semiology of psychogenic noneplileptic seizures revisited: Can video alone predict the diagnosis? Preliminary data from a prospective feasibility study," by G. Erba et al. published in the May 2016 issue of "Epilepsia" journal.

Summary: Objective: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. Methods: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one‐channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full‐array video‐EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video‐EEGs. Results: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%–88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%–100%). The average false detection rate was.53/h (95% CI =.32–.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was.823 (IQR =.57–1). For the total recording duration, F1 score was.74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. Significance: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices. [ABSTRACT FROM AUTHOR]

Phase 2 studies showed that focal seizures could be detected by algorithms using heart rate variability (HRV) in patients with marked autonomic ictal changes. However, wearable surface electrocardiographic (ECG) devices use electrode patches that need to be changed often and may cause skin irritation. We report the first study of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM; Confirm Rx, Abbott). For this proof‐of‐concept (phase 1) study, we recruited six patients admitted to long‐term video‐electroencephalographic monitoring. Fifteen‐minute epochs of ECG signals were saved for each seizure and for control (nonseizure) epochs in the epilepsy monitoring unit (EMU) and in the patients' home environment (1–8 months). We analyzed the ICM signals offline, using a previously developed HRV algorithm. Thirteen seizures were recorded in the EMU, and 41 seizures were recorded in the home‐monitoring period. The algorithm accurately identified 50 of 54 focal seizures (sensitivity = 92.6%, 95% confidence interval [CI] = 85.6%–99.6%). Twelve of the 13 seizures in the EMU were detected (sensitivity = 92.3%, 95% CI = 77.2%–100%), and 38 of the 41 seizures in the out‐of‐hospital setting were detected (sensitivity = 92.7%, 95% CI = 84.7%–100%). Four false detections were found in the 141 control (nonseizure) epochs (false alarm rate = 2.7/24 h). Our results suggest that automated seizure detection using a long‐term, subcutaneous ICM device is feasible and accurate in patients with focal seizures and autonomic ictal changes. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to evaluate the accuracy of a semiautomated classification of nocturnal seizures using a hybrid system consisting of an artificial intelligence‐based algorithm, which selects epochs with potential clinical relevance to be reviewed by human experts. Methods: Consecutive patients with nocturnal motor seizures admitted for video‐electroencephalographic long‐term monitoring (LTM) were prospectively recruited. We determined the extent of data reduction by using the algorithm, and we evaluated the accuracy of seizure classification from the hybrid system compared with the gold standard of LTM. Results: Forty consecutive patients (24 male; median age = 15 years) were analyzed. The algorithm reduced the duration of epochs to be reviewed to 14% of the total recording time (1874 h). There was a fair agreement beyond chance in seizure classification between the hybrid system and the gold standard (agreement coefficient =.33, 95% confidence interval =.20–.47). The hybrid system correctly identified all tonic–clonic and clonic seizures and 82% of focal motor seizures. However, there was low accuracy in identifying seizure types with more discrete or subtle motor phenomena. Significance: Using a hybrid (algorithm–human) system for reviewing nocturnal video recordings significantly decreased the workload and provided accurate classification of major motor seizures (tonic–clonic, clonic, and focal motor seizures). [ABSTRACT FROM AUTHOR]