Unrecognized Focal Nonmotor Seizures in Adolescents Presenting to Emergency Departments.

BACKGROUND AND OBJECTIVES: Many adolescents with undiagnosed focal epilepsy seek evaluation in emergency departments (EDs). Accurate history-taking is essential to prompt diagnosis and treatment. In this study, we investigated ED recognition of motor vs nonmotor seizures and its effect on management and treatment of focal epilepsy in adolescents. METHODS: This was a retrospective analysis of enrollment data from the Human Epilepsy Project (HEP), an international multi-institutional study that collected data from 34 sites between 2012 and 2017. Participants were 12 years or older, neurotypical, and within 4 months of treatment initiation for focal epilepsy. We used HEP enrollment medical records to review participants' initial diagnosis and management. RESULTS: A total of 83 adolescents were enrolled between 12 and 18 years. Fifty-eight (70%) presented to an ED before diagnosis of epilepsy. Although most ED presentations were for motor seizures (n = 52; 90%), many patients had a history of nonmotor seizures (20/52 or 38%). Adolescents with initial nonmotor seizures were less likely to present to EDs (26/44 or 59% vs 32/39 or 82%, p = 0.02), and nonmotor seizures were less likely to be correctly identified (2/6 or 33% vs 42/52 or 81%, p = 0.008). A history of initial nonmotor seizures was not recognized in any adolescent who presented for a first-lifetime motor seizure. As a result, initiation of treatment and admission from the ED was not more likely for these adolescents who met the definition of epilepsy compared with those with no seizure history. This lack of nonmotor seizure history recognition in the ED was greater than that observed in the adult group (0% vs 23%, p = 0.03) and occurred in both pediatric and nonpediatric ED settings. DISCUSSION: Our study supports growing evidence that nonmotor seizures are often undiagnosed, with many individuals coming to attention only after conversion to motor seizures. We found this treatment gap is exacerbated in the adolescent population. Our study highlights a critical need for physicians to inquire about the symptoms of nonmotor seizures, even when the presenting seizure is motor. Future interventions should focus on improving nonmotor seizure recognition for this population in EDs.

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction.

BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. METHOD: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients' quality of life.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures.

AIMS: Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism. METHODS: Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups. RESULTS: All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients. CONCLUSION: Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

The homeostatic effects of the RE-1 silencing transcription factor on cortical networks are altered under ictogenic conditions in the mouse.

AIM: The Repressor Element-1 Silencing Transcription Factor (REST) is an epigenetic master regulator playing a crucial role in the nervous system. In early developmental stages, REST downregulation promotes neuronal differentiation and the acquisition of the neuronal phenotype. In addition, postnatal fluctuations in REST expression contribute to shaping neuronal networks and maintaining network homeostasis. Here we investigate the role of the early postnatal deletion of neuronal REST in the assembly and strength of excitatory and inhibitory synaptic connections. METHODS: We investigated excitatory and inhibitory synaptic transmission by patch-clamp recordings in acute neocortical slices in a conditional knockout mouse model (RestGTi) in which Rest was deleted by delivering PHP.eB adeno-associated viruses encoding CRE recombinase under the control of the human synapsin I promoter in the lateral ventricles of P0-P1 pups. RESULTS: We show that, under physiological conditions, Rest deletion increased the intrinsic excitability of principal cortical neurons in the primary visual cortex and the density and strength of excitatory synaptic connections impinging on them, without affecting inhibitory transmission. Conversely, in the presence of a pathological excitation/inhibition imbalance induced by pentylenetetrazol, Rest deletion prevented the increase in synaptic excitation and decreased seizure severity. CONCLUSION: The data indicate that REST exerts distinct effects on the excitability of cortical circuits depending on whether it acts under physiological conditions or in the presence of pathologic network hyperexcitability. In the former case, REST preserves a correct excitatory/inhibitory balance in cortical circuits, while in the latter REST loses its homeostatic activity and may become pro-epileptogenic.

Oligoepilepsy and lifelong seizure susceptibility in epilepsy with generalized tonic-clonic seizures alone: Experience at an adult tertiary center.

OBJECTIVE: Epilepsy with generalized tonic-clonic seizures alone (GTCA) is the least studied syndrome within the idiopathic generalized epilepsy (IGE) spectrum. We characterize a large cohort of adult patients with GTCA to understand natural history and drug responsiveness. METHODS: In this retrospective single-center study using our epilepsy electronic record, we evaluated clinical characteristics, seizure outcomes, anti-seizure medication (ASM) response including seizure recurrence after ASM withdrawal, and sex differences in a cohort of GTCA patients aged ≥17 years. RESULTS: Within a cohort of 434 IGE patients, 87 patients (20 %) with GTCA were included. The mean age was 34.9 years (range 17-73 years). Forty-six patients (52.8 %) were females. Seventy-two patients (82.8 %) were seizure-free and 15 (17.2 %) had active epilepsy over the previous 12 months. Thirty-four patients (39.1 %) had ≤5 lifetime seizures, aligning with a prior definition of 'oligoepilepsy'. Sixty-five patients (74.7 %) were treated with monotherapy, 19 (21.8 %) were treated with polytherapy, and three were not taking any ASM. Levetiracetam (37.9 %) was the most commonly prescribed ASM, followed by lamotrigine (32.1 %) and valproate (31 %). Seventeen patients (19.5 %) attempted to withdraw their ASM. The rate of seizure recurrence after ASM withdrawal was 88.2 % (15/17), including two patients who relapsed more than 20 years after ASM discontinuation. Females had more seizures in their lifetime and had trialed more ASM compared to males. SIGNIFICANCE: GTCA has a relatively good prognosis, with most patients becoming seizure-free on monotherapy. The high rate of seizure recurrence after ASM withdrawal supports lifetime seizure susceptibility. We found potential sex differences in seizure outcomes and ASM response, although further research is needed to validate this finding.

[Long-term follow-up of adult patients with serial and status course of epileptic seizures]. / Dolgosrochnyi katamnez vzroslykh patsientov s epilepsiei s epilepticheskim statusom ili seriinymi pristupami.

OBJECTIVE: To study the follow-up of adult patients with status epilepticus or a history of serial seizures, assessing the likelihood of achieving long-term remission and identifying predictors of treatment effectiveness. MATERIAL AND METHODS: The study included 280 patients divided into 137 patients with epilepsy with a series of seizures or a history of status epilepticus (group 1) and 143 patients, who had not previously received therapy and did not have a series of seizures or a history of status epilepticus (group 2). A clinical and neurological examination, analysis of medical documentation data, electroencephalography, and MRI were performed. RESULTS: After correction of therapy, remission in patients in group 1 was achieved in 21.9%, improvement in 30%, no effect was observed in 48.1%; in group 2 the indicators were 51%, 28.7%, 20.3%, respectively. The onset of epilepsy in childhood, frequent seizures, and regional epileptiform activity were associated with the lack of treatment effect. CONCLUSION: The results confirm the main role of the clinical examination in determining the prognosis of epilepsy in a particular patient. Currently available instrumental techniques have limited predictive value.

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

Postictal psychiatric symptoms: A neurophysiological study.

OBJECTIVE: Postictal psychiatric symptoms (PPS) are a relatively common but understudied phenomenon in epilepsy. The mechanisms by which seizures contribute to worsening in psychiatric symptoms are unclear. We aimed to identify PPS prospectively during and after admission to the epilepsy monitoring unit (EMU) in order to characterize the postictal physiologic changes leading to PPS. METHODS: We prospectively enrolled patients admitted to the EMU and administered repeat psychometric questionnaires during and after their hospital stay in order to assess for postictal exacerbations in four symptom complexes: anger/hostility, anxiety, depression, and paranoia. Electroclinical and electrographic seizures were identified from the EEG recordings, and seizure durations were measured. The severity of postictal slowing was calculated as the proportion of postictal theta/delta activity in the postictal EEG relative to the preictal EEG using the Hilbert transform. RESULTS: Among 33 participants, 8 demonstrated significant increases in at least one of the four symptoms (the PPS+ group) within three days following the first seizure. The most common PPS was anger/hostility, experienced by 7/8 participants with PPS. Among the 8 PPS+ participants, four experienced more than one PPS. As compared to those without PPS (the PPS- group), the PPS+ group demonstrated a greater degree of postictal EEG slowing at 10 min (p = 0.022) and 20 min (p = 0.05) following seizure termination. They also experienced significantly more seizures during the study period (p = 0.005). There was no difference in seizure duration between groups. SIGNIFICANCE: Postictal psychiatric symptoms including anger/hostility, anxiety, depression, and paranoia may be more common than recognized. In particular, postictal increases in anger and irritability may be particularly common. We provide physiological evidence of a biological mechanism as well as a demonstration of the use of quantitative electroencephalography toward a better understanding of postictal neurophysiology.

Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures.

OBJECTIVE: Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates. METHODS: The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed. RESULTS: When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model. CONCLUSIONS: Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

Low frequency stimulation for seizure suppression: Identification of optimal targets in the entorhinal-hippocampal circuit.

BACKGROUND: Mesial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS) is a common form of drug-resistant focal epilepsy in adults. Treatment for pharmacoresistant patients remains a challenge, with deep brain stimulation (DBS) showing promise for alleviating intractable seizures. This study explores the efficacy of low frequency stimulation (LFS) on specific neuronal targets within the entorhinal-hippocampal circuit in a mouse model of MTLE. OBJECTIVE: Our previous research demonstrated that LFS of the medial perforant path (MPP) fibers in the sclerotic hippocampus reduced seizures in epileptic mice. Here, we aimed to identify the critical neuronal population responsible for this antiepileptic effect by optogenetically stimulating presynaptic and postsynaptic compartments of the MPP-dentate granule cell (DGC) synapse at 1 Hz. We hypothesize that specific targets for LFS can differentially influence seizure activity depending on the cellular identity and location within or outside the seizure focus. METHODS: We utilized the intrahippocampal kainate (ihKA) mouse model of MTLE and targeted specific neural populations using optogenetic stimulation. We recorded intracranial neuronal activity from freely moving chronically epileptic mice with and without optogenetic LFS up to 3 h. RESULTS: We found that LFS of MPP fibers in the sclerotic hippocampus effectively suppressed epileptiform activity while stimulating principal cells in the MEC had no impact. Targeting DGCs in the sclerotic septal or non-sclerotic temporal hippocampus with LFS did not reduce seizure numbers but shortened the epileptiform bursts. CONCLUSION: Presynaptic stimulation of the MPP-DGC synapse within the sclerotic hippocampus is critical for seizure suppression via LFS.

Epileptic spasms in clusters without hypsarrhythmia in infancy and childhood: A single age-dependent type of epilepsy or well-defined epileptic syndrome?

OBJECTIVE: In this study, we present the electroclinical features and outcomes of 92 patients with epileptic spasms (ES) in clusters without modified or classical hypsarrhythmia that started in either in infancy or in childhood; we compared both groups in terms of electroclinical features, etiology, treatment, evolution, and outcome. METHODS: Between June 2000 and July 2022, 92 patients met the electroclinical diagnostic criteria of ES in clusters without hypsarrhythmia. Patients with ES associated with other epileptic encephalopathies including West Syndrome, as well as those with the specific etiology of ES and developmental and epileptic encephalopathy associated with CDKL5 were excluded. RESULTS: The patients were divided into two groups based on the age at ES onset: those with ES onset before (Group 1) and those with ES onset after 2 years of age (Group 2). The features of ES and the type of associated seizures before and after ES onset, as well as the interictal and ictal EEG and electromyography findings were similar in both groups. The etiologies were mainly structural (40.2%), genetic (11.9%), and unknown (44.6%) in majority of the patients in both groups. Thirty-one patients were seizure-free, while in the remaining patients the seizures continued. Nine patients (9.8%) with unilateral structural lesions underwent surgery with good results. The neurological abnormalities and developmental findings prior to ES onset depended on the underlying etiology. CONCLUSION: Our series of patients may represent a well-defined epileptic syndrome or type of epilepsy with onset in infancy or childhood characterized by ES in clusters without hypsarrhythmia associated with focal and generalized seizures and EEG paroxysms without neurological deterioration.

Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation.

OBJECTIVES: Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS. METHODS: We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up. RESULTS: Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures). SIGNIFICANCE: These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.

A scoping review of seizure onset pattern in SEEG and a proposal for morphological classification.

BACKGROUND: Seizure onset pattern (SOP) represents an alteration of electroencephalography (EEG) morphology at the beginning of seizure activity in epilepsy. With stereotactic electroencephalography (SEEG), a method for intracranial EEG evaluation, many morphological SOP classifications have been reported without established consensus. These inconsistent classifications with ambiguous terminology present difficulties to communication among epileptologists. METHODS: We reviewed SOP in SEEG by searching the PubMed database. Reported morphological classifications and the ambiguous terminology used were collected. After thoroughly reviewing all reports, we reconsidered the definitions of these terms and explored a more consistent and simpler morphological SOP classification. RESULTS: Of the 536 studies initially found, 14 studies were finally included after screening and excluding irrelevant studies. We reconsidered the definitions of EEG onset, period for determining type of SOP, core electrode and other terms in SEEG. We proposed a more consistent and simpler morphological SOP classification comprising five major types with two special subtypes. CONCLUSIONS: A scoping review of SOP in SEEG was performed. Our classification may be suitable for describing SOP morphology.

Measurable transitions during seizures in intracranial EEG: A stereoelectroencephalography and SPECT study.

OBJECTIVE: Ictal Single Photon Emission Computed Tomography (SPECT) and stereo-electroencephalography (SEEG) are diagnostic techniques used for the management of patients with drug-resistant focal epilepsies. While hyperperfusion patterns in ictal SPECT studies reveal seizure onset and propagation pathways, the role of ictal hypoperfusion remains poorly understood. The goal of this study was to systematically characterize the spatio-temporal information flow dynamics between differently perfused brain regions using stereo-EEG recordings. METHODS: We identified seizure-free patients after resective epilepsy surgery who had prior ictal SPECT and SEEG investigations. We estimated directional connectivity between the epileptogenic-zone (EZ), non-resected areas of hyperperfusion, hypoperfusion, and baseline perfusion during the interictal, preictal, ictal, and postictal periods. RESULTS: Compared to the background, we noted significant information flow (1) during the preictal period from the EZ to the baseline and hyperperfused regions, (2) during the ictal onset from the EZ to all three regions, and (3) during the period of seizure evolution from the area of hypoperfusion to all three regions. CONCLUSIONS: Hypoperfused brain regions were found to indirectly interact with the EZ during the ictal period. SIGNIFICANCE: Our unique study, combining intracranial electrophysiology and perfusion imaging, presents compelling evidence of dynamic changes in directional connectivity between brain regions during the transition from interictal to ictal states.

Systematic 1 Hz direct electrical stimulation for seizure induction: A reliable method for localizing seizure onset zone and predicting seizure freedom.

OBJECTIVE: To prospectively investigate the utility of seizure induction using systematic 1 Hz stimulation by exploring its concordance with the spontaneous seizure onset zone (SOZ) and relation to surgical outcome; comparison with seizures induced by non-systematic 50 Hz stimulation was attempted as well. METHODS: Prospective cohort study from 2018 to 2021 with ≥ 1 y post-surgery follow up at Yale New Haven Hospital. With 1 Hz, all or most of the gray matter contacts were stimulated at 1, 5, and 10 mA for 30-60s. With 50 Hz, selected gray matter contacts outside of the medial temporal regions were stimulated at 1-5 mA for 0.5-3s. Stimulation was bipolar, biphasic with 0.3 ms pulse width. The Yale Brain Atlas was used for data visualization. Variables were analyzed using Fisher's exact, χ2, or Mann-Whitney test. RESULTS: Forty-one consecutive patients with refractory epilepsy undergoing intracranial EEG for localization of SOZ were included. Fifty-six percent (23/41) of patients undergoing 1 Hz stimulation had seizures induced, 83% (19/23) habitual (clinically and electrographically). Eighty two percent (23/28) of patients undergoing 50 Hz stimulation had seizures, 65% (15/23) habitual. Stimulation of medial temporal or insular regions with 1 Hz was more likely to induce seizures compared to other regions [15/32 (47%) vs. 2/41 (5%), p < 0.001]. Sixteen patients underwent resection; 11/16 were seizure free at one year and all 11 had habitual seizures induced by 1 Hz; 5/16 were not seizure free at one year and none of those 5 had seizures with 1 Hz (11/11 vs 0/5, p < 0.0001). No patients had convulsions with 1 Hz stimulation, but four did with 50 Hz (0/41 vs. 4/28, p = 0.02). SIGNIFICANCE: Induction of habitual seizures with 1 Hz stimulation can reliably identify the SOZ, correlates with excellent surgical outcome if that area is resected, and may be superior (and safer) than 50 Hz for this purpose. However, seizure induction with 1 Hz was infrequent outside of the medial temporal and insular regions in this study.

Emergency medical service response for cases of stroke-suspected seizure: A population-based study.

OBJECTIVES: We evaluated the on-scene time of emergency medical services (EMS) for cases where discrimination between acute stroke and epileptic seizures at the initial examination was difficult and identified factors linked to delays in such scenarios. MATERIALS AND METHODS: A retrospective review of cases with suspected seizure using the EMS database of fire departments across six Japanese cities between 2016 and 2021 was conducted. Patient classification was based on transport codes. We defined cases with stroke-suspected seizure as those in whom epileptic seizure was difficult to differentiate from stroke and evaluated their EMS on-scene time compared to those with epileptic seizures. RESULTS: Among 30,439 cases with any seizures, 292 cases of stroke-suspected seizure and 8,737 cases of epileptic seizure were included. EMS on-scene time in cases of stroke-suspected seizure was shorter than in those with epileptic seizure after propensity score matching (15.1±7.2 min vs. 17.0±9.0 min; p = 0.007). Factors associated with delays included transport during nighttime (odds ratio [OR], 1.73, 95 % confidence interval [CI] 1.02-2.93, p = 0.041) and transport during the 2020-2021 pandemic (OR, 1.77, 95 % CI 1.08-2.90, p = 0.022). CONCLUSION: This study highlighted the difference between the characteristics in EMS for stroke and epileptic seizure by evaluating the response to cases with stroke-suspected seizure. Facilitating prompt and smooth transfers of such cases to an appropriate medical facility after admission could optimize the operation of specialized medical resources.

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE: Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients. METHODS: We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies. RESULTS: A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test). SIGNIFICANCE: Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.