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Objective This study aimed to survey current practices in European epilepsy monitoring units (EMUs) with emphasis on safety issues. Methods A 37-item questionnaire investigating characteristics and organization of EMUs, including measures for prevention and management of seizure-related serious adverse events (SAEs), was distributed to all identified European EMUs plus one located in Israel (N = 150). Results Forty-eight (32%) EMUs, located in 18 countries, completed the questionnaire. Epilepsy monitoring unit beds are 1–2 in 43%, 3–4 in 34%, and 5–6 in 19% of EMUs; staff physicians are 1–2 in 32%, 3–4 in 34%, and 5–6 in 19% of EMUs. Personnel operating in EMUs include epileptologists (in 69% of EMUs), clinical neurophysiologists trained in epilepsy (in 46% of EMUs), child neurologists (in 35% of EMUs), neurology and clinical neurophysiology residents (in 46% and in 8% of EMUs, respectively), and neurologists not trained in epilepsy (in 27% of EMUs). In 20% of EMUs, patients' observation is only intermittent or during the daytime and primarily carried out by neurophysiology technicians and/or nurses (in 71% of EMUs) or by patients' relatives (in 40% of EMUs). Automatic detection systems for seizures are used in 15%, for body movements in 8%, for oxygen desaturation in 33%, and for ECG abnormalities in 17% of EMUs. Protocols for management of acute seizures are lacking in 27%, of status epilepticus in 21%, and of postictal psychoses in 87% of EMUs. Injury prevention consists of bed protections in 96% of EMUs, whereas antisuffocation pillows are employed in 21%, and environmental protections in monitoring rooms and in bathrooms are implemented in 38% and in 25% of EMUs, respectively. The most common SAEs were status epilepticus reported by 79%, injuries by 73%, and postictal psychoses by 67% of EMUs. Conclusions All EMUs have faced different types of SAEs. Wide variation in practice patterns and lack of protocols and of precautions to ensure patients' safety might promote the occurrence and severity of SAEs. Our findings highlight the need for standardized and shared protocols for an effective and safe management of patients in EMUs. [ABSTRACT FROM AUTHOR]

• The most prevalent seizure-onset patterns (SOPs) are low frequency periodic spikes (LFPS) and low-voltage fast activity (LVFA). • LFPS had the most divergent terminology, whereas LVFA had the most reproducible terminology. • Some SOP terms were inconsistent with standard EEG terminology. 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. 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. 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. 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. These results underline the need for standardization of SEEG terminology. [ABSTRACT FROM AUTHOR]

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 65.27 % for 182 seizures from the CHB-MIT dataset and 57.26 % 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 93.95 % (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms—up to 96 % 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG is a promising biomarker for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. Methods We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the video-EEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients (356 recording hours) were used as training set of data to optimize the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify and optimize an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Q- and S-peaks can create in the tachogram, which causes error in short-term HRV-analysis. Results The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value ( P +  = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch®ECG device, when testing the same dataset. Conclusion The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy. The R-peak detection algorithm is the first important step in creating a portable fully automatic real-time seizure detection for these patients. [ABSTRACT FROM AUTHOR]

Behavior during 3–4 Hz spike‐wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)‐based machine‐learning method to predict SWD‐related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10‐fold cross‐validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG‐based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures. [ABSTRACT FROM AUTHOR]

• This clinical practice guideline identified standards with recommendations summarized in Table S1. • Limited high-level evidence addressing standards for LTVEM exist, and further research is needed. • Selected topics for utility of LTVEM exist, although comprehensive criteria addressing minimum standards for performance are needed. • Clinicians, hospital administrators, and insurers benefit from establishing standards for inpatient video-EEG monitoring applied to patient management. The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility. [ABSTRACT FROM AUTHOR]

Summary: The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long‐term video‐electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta‐Analysis) statement. We found limited high‐level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long‐term outcome effects of LTVEM and to establish its clinical utility. [ABSTRACT FROM AUTHOR]

Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions. [ABSTRACT FROM AUTHOR]

Background: Exploding head syndrome is a rarely reported benign sensory parasomnia that may nonetheless have significant impact on patients' quality of life and their perceived well-being. To date, the mechanisms underlying attacks, characterised by a painless perception of abrupt, loud noises at transitional sleep-wake or wake-sleep states, are by and large unclear.Methods and Results: In order to address the current gap in the knowledge of potential underlying pathophysiology, a retrospective case-control study of polysomnographic recordings of patients presenting to a tertiary sleep disorders clinic with exploding head syndrome was conducted. Interictal (non-attack associated) electroencephalographic biomarkers were investigated by performing macrostructural and event-related dynamic spectral analyses of the whole-night EEG. In patients with exploding head syndrome, additional oscillatory activity was recorded during wakefulness and at sleep/wake periods. This activity differed in its frequency, topography and source from the alpha rhythm that it accompanied.Conclusion: Based on these preliminary findings, we hypothesise that at times of sleep-wake transition in patients with exploding head syndrome, aberrant attentional processing may lead to amplification and modulation of external sensory stimuli. [ABSTRACT FROM AUTHOR]

• Local and distant connections are differently modulated by sleep. • Clear interhemispheric difference in epileptogenic areas' connectivity during sleep. • Different patterns of information flow during sleep in physiologic versus pathologic structures. Sleep is an active process with an important role in memory. Epilepsy patients often display a disturbed sleep architecture, with consequences on cognition. We aimed to investigate the effect of sleep on cortical networks' organization. We analyzed cortico-cortical evoked responses elicited by single pulse electrical stimulation (SPES) using intracranial depth electrodes in 25 patients with drug-resistant focal epilepsy explored using stereo-EEG. We applied the SPES protocol during wakefulness and NREM – N2 sleep. We analyzed 31,710 significant responses elicited by 799 stimulations covering most brain structures, epileptogenic or non-epileptogenic. We analyzed effective connectivity between structures using a graph-theory approach. Sleep increases excitability in the brain, regardless of epileptogenicity. Local and distant connections are differently modulated by sleep, depending on the tissue epileptogenicity. In non-epileptogenic areas, frontal lobe connectivity is enhanced during sleep. There is increased connectivity between the hippocampus and temporal neocortex, while perisylvian structures are disconnected from the temporal lobe. In epileptogenic areas, we found a clear interhemispheric difference, with decreased connectivity in the right hemisphere during sleep. Sleep modulates brain excitability and reconfigures functional brain networks, depending on tissue epileptogenicity. We found specific patterns of information flow during sleep in physiologic and pathologic structures, with possible implications for cognition. [ABSTRACT FROM AUTHOR]

The objective of the study was to investigate electroclinical and neuropsychological features, genetic background, and evolution of children with idiopathic encephalopathy with status epilepticus during slow sleep (ESES), including Landau–Kleffner syndrome (LKS). All children diagnosed with idiopathic ESES at the Danish Epilepsy Centre between March 2003 and December 2014 were retrospectively reviewed. Repeated 24-hour electroencephalography (24-h EEG) recordings, neuropsychological assessments, and clinical–neurological evaluation were performed throughout the follow-up in all patients. In 13 children, genetic investigations were performed. We collected 24 children (14 males and 10 females). Mean age at ESES diagnosis was 6 years, and mean ESES duration was 2 years and 7 months. Twenty-one children had epileptic seizures. Three children had LKS. Topography of sleep-related EEG epileptic abnormalities was diffuse in 3 subjects, hemispheric in 6, multifocal in 9, and focal in 6. During the active phase of ESES, all children presented with a heterogeneous combination of behavioral and cognitive disturbances. In 14 children, a parallel between severity of the clinical picture and spike–wave index (SWI) was observed. We could not find a strict correlation between the type and severity of neurobehavioral impairment and the side/topography of sleep-related EEG discharges during the active phase of ESES. At the last follow-up, 21 children were in remission from ESES. Complete recovery from neurobehavioral disorders was observed in 5 children. Genetic assessment, performed in 13 children, showed GRIN2A variant in two (15.4%). Our patients with idiopathic ESES showed a heterogeneous pattern of epileptic seizures, neurobehavioral disorders, and sleep EEG features. Only one-fourth of children completely recovered from the neuropsychological disturbances after ESES remission. Lack of correlation between severity/type of cognitive derangement and SWI and/or topography of sleep EEG epileptic abnormalities may suggest the contribution of additional factors (including impaired sleep homeostasis due to epileptic activity) in the neurobehavioral derangement that characterize ESES. • Patients with idiopathic ESES show a variety of seizures, neurobehavioral disorders, and EEG features. • Only one-fourth of children completely recovered after ESES remission. • Protracted sleep-related epileptiform activity is harmful in the developmental age. • Impairment of sleep homeostasis might play a role in ESES pathophysiology. • Idiopathic ESES is a model of epileptic encephalopathy due to protracted epileptic activity. [ABSTRACT FROM AUTHOR]

Summary: Sudden unexpected death in epilepsy (SUDEP) is most often associated with the occurrence of generalized tonic–clonic seizures (GTCS), a seizure type that can now be detected with high sensitivity and specificity by wearable or bed devices. The recent development in such devices and their performance offer multiple opportunities to tackle SUDEP and its prevention. Reliable GTCS detection might help physicians optimize antiepileptic treatment, which could in turn reduce the risk of SUDEP. GTCS‐triggered alarms can lead to immediate intervention by caregivers that are also likely to decrease the odd of SUDEP. The biosignals used to detect GTCS might provide novel SUDEP biomarkers, in particular, by informing on several important characteristics of the ictal and postictal periods (type of GTCS, duration of tonic phase, rotation in the prone position, presence and duration of postictal immobility and bradycardia, rise in electrodermal activity). Other biosensors not yet used for detecting GTCS might provide complementary information, such as the presence and intensity of ictal/postictal hypoxemia. The above biomarkers, if strongly predictive, could help identify patients at very high risk of SUDEP, enabling better assessment of individual risk, as well as selection of appropriate patients for clinical studies aiming at preventing SUDEP. The same biosignals could also be used as ancillary biomarkers to test the impact of various interventions before moving to highly challenging randomized controlled trials with SUDEP as a primary outcome. [ABSTRACT FROM AUTHOR]

Objective To assess inter-rater agreement on EEG-reactivity (EEG-R) in comatose patients and compare it with a quantitative method (QEEG-R). Methods Six 30-s stimulation epochs (noxious, visual and auditory) were performed during EEG on 19 neurosurgical and 11 cardiac arrest patients. Six experts analysed EEGs for reactivity using their habitual methods. QEEG-R was defined as present if ≥2/6 epochs were reactive (stimulation/rest power ratio exceeding noise level). Three-months patient outcome was assessed by the Cerebral Performance Category Score (CPC) dichotomized in good (1–2) or poor (3–5). Results Agreement among experts on overall EEG-R varied from 53% to 83% ( κ : 0.05–0.64) and reached 100% ( κ : 1) between two QEEG-R calculators. For the experts, absence of EEG-R yielded sensitivities for poor outcome between 40–85% and specificities between 20–90%, for QEEG-R sensitivity was 40% (CI: 23–68%) and specificity 100% (CI: 69–100%). Conclusions There is a large inter-rater variation among experts on EEG-R assessment in comatose patients. QEEG-R is a promising objective prognostic parameter with low inter-rater variation and a high specificity for prediction of poor outcome. Significance Clinicians should be cautious when using the traditional, qualitative method, in particular in end-of-life decisions. Implementation of the quantitative method in clinical practice may improve reliability of reactivity assessments. [ABSTRACT FROM AUTHOR]

Precipitation and inhibition of seizures and epileptic discharges by sensory stimuli are receiving increasing attention because they provide insight into natural seizure generation in human epilepsies and can identify potential nonpharmacological therapies. We aimed to investigate modulation (provocation or inhibition) of epileptiform discharges (EDs) in mesial temporal lobe epilepsy (MTLE) versus idiopathic generalized epilepsy (IGE) by olfactory stimulation (OS) compared with standard provocation methods. The underlying hypothesis was that any response would be more likely to occur in MTLE, considering the anatomical connections of the temporal lobe to the olfactory system. This multicenter, international study recruited patients with either MTLE or IGE who were systematically compared for responses to OS using an EEG/video-EEG protocol including a 30-min baseline, twice 3-min olfactory stimulation with ylang-ylang, hyperventilation, and intermittent photic stimulation. The 95% confidence interval (CI) for the baseline EDs in each patient was calculated, and modulation was assumed when the number of EDs during any 3-min test period was outside this CI. A total of 134 subjects (55 with MTLE, 53 with IGE, and 26 healthy controls) were included. Epileptiform discharges were inhibited during OS in about half the patients with both MTLE and IGE, whereas following OS, provocation was seen in 29.1% of patients with MTLE and inhibition in 28.3% of patients with IGE. Olfactory stimulation was less provocative than standard activation methods. The frequent subclinical modulation of epileptic activity in both MTLE and IGE is in striking contrast with the rarity of reports of olfactory seizure precipitation and arrest. Inhibition during OS can be explained by nonspecific arousal. The delayed responses seem to be related to processing of olfactory stimuli in the temporal lobe, thalamus, and frontal cortex. [ABSTRACT FROM AUTHOR]

Objective To investigate default-mode network (DMN) deactivation in different types of focal epilepsy by single pulse electric stimulation (SPES). Method We analyzed 18 patients with focal epilepsy (8 frontal, 8 temporal, 2 posterior), implanted with depth electrodes. SPES was applied to each pair of adjacent contacts in seiwure onset zone (SOZ), and responses were recorded from the 10 different DMN-hubs; The responses to SPES in Gamma, Ripple (R) and Fast-Ripple (FR) bands were quantified in a 60–250 ms time-window following each stimulation pulse and compared to baseline. Time of propagation was determined based on N1 latency. These responses were compared across the three epilepsy types. Results Overall, we observed a significant deactivation of the DMN in the upper frequency ranges for all epilepsies (75% drop from baseline for gamma and 93% for R and FR). In the gamma band, the stimulation of frontal SOZs deactivated the ipsilateral posterior cingulate and precuneus significantly more than in the other epilepsy-types, with shorter general latencies of 22, 25 and 23 ms, respectively. Discussion Our results might explain why in prefrontal focal epilepsies and in generalized epilepsies, thought to have a frontal generator, the consciousness is preferentially and significantly impaired. Significance This is a promising method for quantifying the engagement of the consciousness processing areas by the epileptic focus. Detailed illustrations and tables for all 10 DMN-hubs, in all 3 high-frequency bands are provided. [ABSTRACT FROM AUTHOR]

The full impact of multisystem disease such as obstructive sleep apnoea (OSA) on regions of the central nervous system is debated, as the subsequent neurocognitive sequelae are unclear. Several preclinical studies suggest that its purported major culprits, intermittent hypoxia and sleep fragmentation, can differentially affect adult hippocampal neurogenesis. Although the prospective biphasic nature of chronic intermittent hypoxia in animal models of OSA has been acknowledged, so far the evidence for increased ‘compensatory’ neurogenesis in humans is uncertain. In a cross-sectional study of 32 patients with mixed severity OSA and 32 non-apnoeic matched controls inferential analysis showed bilateral enlargement of hippocampi in the OSA group. Conversely, a trend for smaller thalami in the OSA group was noted. Furthermore, aberrant connectivity between the hippocampus and the cerebellum in the OSA group was also suggested by the correlation analysis. The role for the ischemia/hypoxia preconditioning in the neuropathology of OSA is herein indicated, with possible further reaching clinical implications. [ABSTRACT FROM AUTHOR]