Epileptic Events Research Articles

Volumetric optoacoustic neurobehavioral tracking of epileptic seizures in freely-swimming zebrafish larvae.

Fast three-dimensional imaging of freely-swimming zebrafish is essential to understand the link between neuronal activity and behavioral changes during epileptic seizures. Studying the complex spatiotemporal patterns of neuronal activity at the whole-brain or -body level typically requires physical restraint, thus hindering the observation of unperturbed behavior. Here we report on real-time volumetric optoacoustic imaging of aberrant circular swimming activity and calcium transients in freely behaving zebrafish larvae, continuously covering their motion across an entire three-dimensional region. The high spatiotemporal resolution of the technique enables capturing ictal-like epileptic seizure events and quantifying their propagation speed, independently validated with simultaneous widefield fluorescence recordings. The work sets the stage for discerning functional interconnections between zebrafish behavior and neuronal activity for studying fundamental mechanisms of epilepsy and in vivo validation of treatment strategies.

Application of a Head-Up Tilt Table Test in Differentiation Between Epilepsy and Syncope in Children

Background: Distinguishing between seizure and neurally mediated syncope is challenging because of similar consequences and medical history. A head-up tilt test (HUTT) is a non-invasive, simple, and easy test to distinguish between epilepsy and syncope besides detailed history taking. Objectives: This study aimed to differentiate between epileptic events and reflex syncope (any different type of syncope) using the head-upright tilt test. Methods: We studied 59 patients (37 boys and 22 girls) between 4 to 18 years old (mean age, 10.5 ± 3.7 years) with a previous diagnosis of seizure who did not respond well to treatment. All patients underwent HUTT, and the test was positive in 26 patients. There were no significant differences in sex, age, provocative factors, associated syndrome, and family history between negative and positive groups. Results: There was a history of actual syncope in 26.9% of the positive tilt test group compared to 15.15% of the negative test group. Also, there was a positive family history of syncope in the positive tilt test group. Among 26 patients with a positive tilt test, 17 were diagnosed with vasovagal syncope (VVS) vasodepressor type and 9 with mixed type. Antiepileptic drugs were tapered for patients diagnosed with VVS, and they did not show any seizures after 18 ± 6 months of follow-up. Overemphasizing positive family history and inattention to history taking are 2 crucial factors leading to the misdiagnosis of epilepsy. Conclusions: Our study showed that HUTT is a non-invasive test that can be useful, especially for early and proper diagnosis in children with refractory epilepsy.

Seizure after surgical treatment of chronic subdural hematoma-Associated factors and effect on outcome.

IntroductionChronic subdural hematoma (cSDH) is becoming more prevalent due to population aging and the increasing use of antithrombotic drugs. Postoperative seizure in cSDH have a negative effect on outcome, and there currently no consensus regarding prophylactic anti-epileptic drug (AED) treatment. The objective of this study was to evaluate predisposing and triggering factors associated with postoperative epileptic seizure in patients with cSDH.MethodsAll patients, who were surgically treated for cSDH in a single tertiary care center between 2015 and 2019, were considered for inclusion. Relevant patient- and hematoma-specific characteristics were retrospectively extracted from hospital records. Paroxysmal events categorized by the treating physician as suspected postoperative seizures were noted. The clinical outcome was extracted from the last available follow-up visit and classified according to the Glasgow outcome scale (GOS).ResultsOf the included 349 patients, 54 (15.5%) developed suspected postoperative epileptic complications in the form of early seizure (≤ 7 days) in 11 patients (3.2%) and late seizure (>7 days) in 43 patients (12.3%). In the logistic regression analysis, solely depressed brain volume (supratentorial volume (ml) not filled with re-expanded brain) was independently associated with postoperative seizure (odds ratio [OR] 1.006, 95% CI: 1.001–1.011; p = 0.034). The occurrence of postoperative seizure (OR 6.210, 95% CI: 2.704–14.258; p < 0.001) and preoperative Markwalder grading (OR 2.919, 95% CI: 1.538–5.543; p = 0.001) were independently associated with unfavorable (GOS1−3) outcome.ConclusionLarger postoperative depressed brain volume was the only factor independently associated with suspected postoperative seizure, and it could help identify a subgroup of patients with higher susceptibility to epileptic events. Based on our data, no formal recommendation can be made regarding the prophylactic use of anti-epileptic drugs. Nevertheless, the relative safety of new generation AEDs and the detrimental effect of postoperative seizure on outcome may justify its use in a selected patient population.

Investigations in children with seizures visiting a pediatric emergency department: A monocenter study.

Neurological disorders, in particular seizure, are one of the reasons for admission in pediatric emergency departments (PED). We aimed to evaluate the frequency and the relevance of each investigation for seizure management in the PED. We conducted a one-year retrospective study. Based on predefined criteria, we evaluate the appropriateness of the investigations. Logical regression was used to study the risk factors for acute symptomatic seizure (ASS). We identified 691 visits to the PED for an epileptic event over an annual volume of 80,320 visits. Seizures occurring in Children with epilepsy were the most frequent epileptic events seen in the PED (42%). Looking at the investigation performed in the PED, a blood electrolytes analysis was performed in 26%, neuroimaging in 9%, electroencephalography recording in 9% and LP in 5% of patients. ASSs represented 2.1% of the seizures and 0.6% of PED neurological visits. In the multivariate analysis, an initial abnormal neurological examination (OR, 20.92 [4.87; 89.81, p<0.0001) was the only risk factor that remained significantly associated with ASS. A seizure occurring in an epilepsy patient was significantly associated with an unprovoked seizure (OR, 0.12 [0.02; 0.57], p<0.008). All ASSs were associated with a positive or abnormal examination. Moreover, there is a significant proportion of investigations requested in cases of an epileptic event that did not lead to a diagnosis or modification of the management. Based on our methods, there seems to be an overuse of investigations for seizure in children with epilepsy.

What is the optimal duration of home-video-EEG monitoring for patients with

Ambulatory “at home” video-EEG monitoring (HVEM) may offer a more cost-effective and accessible option as compared to traditional inpatient admissions to epilepsy monitoring units. However, home monitoring may not allow for safe tapering of anti-seizure medications (ASM). As a result, longer periods of monitoring may be necessary to capture a sufficient number of the patients' stereotypic seizures. We aimed to quantitatively estimate the necessary length of HVEM corresponding to various diagnostic scenarios in clinical practice. Using available seizure frequency statistics, we estimated the HVEM duration required to capture one, three, or five seizures on different days, by simulating 100,000 annual time-courses of seizure occurrence in adults and children with more than one and <30 seizures per month (89% of adults and 85% of children). We found that the durations of HVEM needed to record 1, 3, or 5 seizures in 80% of children were 2, 5, and 8 weeks (median 2, 12, and 21 days), respectively, and significantly longer in adults −2, 6, and 10 weeks (median 3, 14, and 26 days; p < 10−10 for all comparisons). Thus, longer HVEM than currently used is needed for expanding its clinical value from diagnosis of nonepileptic or very frequent epileptic events to a presurgical tool for patients with drug-resistant epilepsy. Technical developments and further studies are warranted.

Predictive value of video alone in diagnosis of epileptic vs paroxysmal nonepileptic events in children

ObjectivePrevious studies examined the use of video-based diagnosis and the predictive value of videos for differentiation of epileptic seizures (ES) from paroxysmal nonepileptic events (PNEE) in the adult population. However, there are no such published studies strictly on the pediatric population. Using video-EEG diagnosis as a gold standard, we aimed to determine the diagnostic predictive value of videos of habitual events with or without additional clinical data in differentiating the PNEE from ES in children. MethodsConsecutive admissions to our epilepsy monitoring unit between June 2020 and December 2020 were analyzed for events of interest. Four child neurologists blinded to the patient’s diagnosis formulated a diagnostic impression based upon the review of the video alone and again after having access to basic clinical information, in addition to the video. Features of the video which helped to make a diagnosis were identified by the reviewers as a part of a survey. ResultsA total of 54 patients were included (ES n = 24, PNEE n = 30). Diagnostic accuracy was calculated for each reviewer and combined across all the ratings. Diagnostic accuracy by video alone was 74.5% (sensitivity 80.8%, specificity 66.7%). Providing reviewers with basic clinical information in addition to the videos significantly improved diagnostic accuracy compared to viewing the videos alone. Inter-rater reliability between four reviewers based on the video alone showed moderate agreement (κ = 0.51) and unchanged when additional clinical data were presented (κ = 0.51). The ES group was significantly more likely to demonstrate changes in facial expression, generalized stiffening, repetitive eye blinks, and eye deviation when compared with the PNEE group, which was more likely to display bilateral myoclonic jerking. ConclusionsVideo review of habitual events by Child Neurologists may be helpful in reliably distinguishing ES from PNEE in children, even without included clinical information.

Safety and efficacy of cannabidiol-cannabidiolic acid rich hemp extract in the treatment of refractory epileptic seizures in dogs.

The use of cannabidiol (CBD) in childhood refractory seizures has become a common therapeutic approach for specific seizure disorders in human medicine. Similarly, there is an interest in using CBD, cannabidiolic acid (CBDA) or cannabinoid-rich hemp products in the treatment of idiopathic epilepsy in dogs. We aimed to examine a small cohort in a pilot investigation using a CBD and CBDA-rich hemp product for the treatment of refractory epileptic seizures in dogs. Fourteen dogs were examined in a 24-week randomized cross-over study being provided placebo or CBD/CBDA-rich hemp extract treatment at 2 mg/kg orally every 12 h for each 12-week arm of the study. Serum chemistry, complete blood counts, serum anti-seizure medication (ASM) concentrations and epileptic seizure frequency were followed over both arms of the cross-over trial. Results demonstrated that besides a mild increase in alkaline phosphatase, there were no alterations observed on routine bloodwork at 2, 6, and 12 weeks during either arm of the study. Epileptic seizure frequency decreased across the population from a mean of 8.0 ± 4.8 during placebo treatment to 5.0 ± 3.6 with CBD/CBDA-rich hemp extract (P = 0.02). In addition, epileptic seizure event days over the 12 weeks of CBD/CBDA-rich hemp treatment were 4.1 ± 3.4, which was significantly different than during the 12 weeks of placebo treatment (5.8 ± 3.1; P =0.02). The number of dogs with a 50% reduction in epileptic activity while on treatment were 6/14, whereas 0/14 had reductions of 50% or greater while on the placebo (P = 0.02). No differences were observed in serum zonisamide, phenobarbital or bromide concentrations while on the treatment across groups. Adverse events were minimal, but included somnolence (3/14) and transient increases in ataxia (4/14) during CBD/CBDA-rich hemp extract treatment; this was not significantly different from placebo. This further indicates that providing CBD/CBDA-rich hemp extract during refractory epilepsy (only partially responsive to ASM), in conjunction with other ASM appears safe. Based on this information, the use of 2 mg/kg every 12 h of a CBD/CBDA-rich hemp extract can have benefits in reducing the incidence of epileptic seizures, when used concurrently with other ASMs.

Extreme value theory inspires explainable machine learning approach for seizure detection

Epilepsy is one of the brightest manifestations of extreme behavior in living systems. Extreme epileptic events are seizures, that arise suddenly and unpredictably. Usually, treatment strategies start by analyzing brain activity during the seizures revealing their type and onset mechanisms. This approach requires collecting data for a representative number of events which is only possible during the continuous EEG monitoring over several days. A big part of the further analysis is searching for seizures on these recordings. An experienced medical specialist spends hours checking the data of a single patient and needs assistance from the automative systems for seizure detection. Machine learning methods typically address this issue in a supervised fashion and exhibit a lack of generalization. The extreme value theory allows addressing this issue with the unsupervised machine learning methods of outlier detection. Here, we make the first step toward using this approach for the seizure detection. Based on our recent work, we specified the EEG features showing extreme behavior during seizures and loaded them to the one-class SVM, a popular outlier detection algorithm. Testing the proposed approach on 83 patients, we reported 77% sensitivity and 12% precision. In 60 patients, sensitivity was 100%. In the rest 23 subjects, we observed deviations from the extreme behavior. The one-class SVM used a single subject’s data for training; therefore, it was stable against between-subject variability. Our results demonstrate an effective convergence between the extreme value theory, a physical concept, and the outlier detection algorithms, a machine learning concept, toward solving the meaningful task of medicine.

A time-reversed model selection approach to time series forecasting

In this paper, we introduce a novel model selection approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model parameters. By combining theoretical principles of time-reversibility in time series with conventional modeling approaches such as information criteria, we construct a criterion that employs the backwards prediction (backcast) as a proxy for the forecast. Hereby, we aim to adopt a theoretically grounded, data-driven approach to model selection. The novel criterion is named the backwards validated information criterion (BVIC). The BVIC identifies suitable models by trading off a measure of goodness-of-fit and a models ability to predict backwards. We test the performance of the BVIC by conducting experiments on synthetic and real data. In each experiment, the BVIC is examined in contrast to conventionally employed criteria. Our experimental results suggest that the BVIC has comparable performance as conventional information criteria. Specifically, in most of the experiments performed, we did not find statistically significant differences between the forecast error of the BVIC under certain parameterizations and that of the different information criteria. Nonetheless, it is worth emphasizing that the BVIC guarantees are established by design where the model order penalization term depends on strong mathematical properties of time-reversible time series forecasting properties and a finite data assessment. In particular, the penalization term is replaced by a weighted trade-off between functional dimensions pertaining to forecasting.That said, we observed that the BVIC recovered more accurately the real order of the underlying process than the other criteria, which rely on a static penalization of the model order. Lastly, leveraging the latter property we perform the assessment of the order model (or, memory) of time series pertaining to epileptic seizures recorded using electrocorticographic data. Our results provide converging evidence that the order of the model increases during the epileptic events.

Computational attributes of protein kinase-C gamma C2-domain & virtual screening for small molecules: elucidation from meta-dynamics simulations & free-energy calculations

Epilepsy, a moderate to chronic neuropathological condition, is induced by the acute blockage of synaptic and voltage-gated inhibitory conduction or through the activation of synaptic and voltage-gated excitatory conduction. The regulation of long-term potentiation (LTP) is important in the regulation of epileptic events, and its activity is linked to specific protein kinases. The PKC-γ subtype is a vaguely explored therapeutic target for neurological disorders, but in selected studies, it is proven to be a critical intermediate protein in LTP. This study utilized computational modelling approaches including receptor-based docking, QSAR followed by explicit binding score assessment method MM/GBSA, MM/PBSA (EDA) and MTD simulation-based FES iteration. This was performed to virtually screen the small molecule libraries, which comprised about 2.79 lacs compounds against the Ca2+-binding site of the PKC-γ-C2 regulatory domain. The screened molecules LIG-41 ([4-Oxo-4-(4-phenylmethoxyanilino) butyl] azanium) and LIG-16 (Emixustat) exhibit overall optimal attributes in the above-mentioned parameters. The two leads are expected to inhibit the Ca2+-mediated PKC-γ activity. Communicated by Ramaswamy H. Sarma

Diffusion tractography predicts propagated high-frequency activity during epileptic spasms.

To determine the structural networks that constrain propagation of ictal oscillations during epileptic spasm events, and compare the observed propagation patterns across patients with successful or unsuccessful surgical outcomes. Subdural electrode recordings of 18 young patients (age 1-11years) were analyzed during epileptic spasm events to determine ictal networks and quantify the amplitude and onset time of ictal oscillations across the cortical surface. Corresponding structural networks were generated with diffusion magnetic resonance imaging (MRI) tractography by seeding the cortical region associated with the earliest average oscillation onset time, and white matter pathways connecting active electrode regions within the ictal network were isolated. Properties of this structural network were used to predict oscillation onset times and amplitudes, and this relationship was compared across patients who did and did not achieve seizure freedom following resective surgery. Onset propagation patterns were relatively consistent across each patient's spasm events. An electrode's average ictal oscillation onset latency was most significantly associated with the length of direct corticocortical tracts connecting to the area with the earliest average oscillation onset (p<.001, model R2 =.54). Moreover, patients demonstrating a faster propagation of ictal oscillation signals within the corticocortical network were more likely to have seizure recurrence following resective surgery (p=.039). In addition, ictal oscillation amplitude was associated with connecting tractography length and weighted fractional anisotropy (FA) measures along these pathways (p=.002/.030, model R2 =.31/.25). Characteristics of analogous corticothalamic pathways did not show significant associations with ictal oscillation onset latency or amplitude. Spatiotemporal propagation patterns of high-frequency activity in epileptic spasms align with length and FA measures from onset-originating corticocortical pathways. Considering the data in this individualized framework may help inform surgical decision-making and expectations of surgical outcomes.

Sleep: The Tip of the Iceberg in the Bidirectional Link Between Alzheimer's Disease and Epilepsy

The observation that a pathophysiological link might exist between Alzheimer's disease (AD) and epilepsy dates back to the identification of the first cases of the pathology itself and is now strongly supported by an ever-increasing mountain of literature. An overwhelming majority of data suggests not only a higher prevalence of epilepsy in Alzheimer's disease compared to healthy aging, but also that AD patients with a comorbid epileptic syndrome, even subclinical, have a steeper cognitive decline. Moreover, clinical and preclinical investigations have revealed a marked sleep-related increase in the frequency of epileptic activities. This characteristic might provide clues to the pathophysiological pathways underlying this comorbidity. Furthermore, the preferential sleep-related occurrence of epileptic events opens up the possibility that they might hasten cognitive decline by interfering with the delicately orchestrated synchrony of oscillatory activities implicated in sleep-related memory consolidation. Therefore, we scrutinized the literature for mechanisms that might promote sleep-related epileptic activity in AD and, possibly dementia onset in epilepsy, and we also aimed to determine to what degree and through which processes such events might alter the progression of AD. Finally, we discuss the implications for patient care and try to identify a common basis for methodological considerations for future research and clinical practice.

Reflex seizures in rare monogenic epilepsies.

Reflex seizures (RSs) are epileptic events consistently induced by specific triggers. They occur in epilepsies of varied etiologies and are often accompanied by spontaneous seizures. The genetic background of RSs is heterogeneous and polygenic or multifactorial inheritance is suspected in the majority of cases. Although causative single-gene variants are rarely identified, the number of genes associated with RSs is gradually increasing. In this article, we describe individuals presenting reflex seizures as predominant epileptic events in whom we identified pathogenic and likely pathogenic variants in CACNA1A, GNAO1, and NOVA2 genes. In addition, we summarize rare monogenic epilepsies associated with RSs. The presence of RSs in our patients expands the phenotypic spectrum of the diseases and contributes to our knowledge of the underlying monogenic defects in reflex seizures.

Prognosis of Epileptic Seizure Event Onsets Using Random Survival Forests

This article introduces a machine learning approach, based on a nonparametric, decision-tree-based random survival forest (RSF) model, for a continuous prognosis of epileptic seizure events using electroencephalogram (EEG) data. While earlier seizure prediction methods forecast seizure occurrences only at a specified future time, the RSF model allows estimation of the probability of seizure onset, in terms of a hazard function, over the entire prediction horizon. These estimates are crucial for developing individualized quantitative risk measures and management plans for epilepsy patients. Additionally, RSF can identify the key risk factors by capturing the interdependencies among the features extracted from EEG data. The performance of RSF was evaluated for prognosing seizure onsets of the rat and mice specimens in an 80 small animals cohort at the Texas A&M Department of Neuroscience and Experimental Therapeutics. The results suggest that RSF outperforms other contemporary survival models, including the popular Cox proportional hazard, with 87.5% lower integrated Brier Score (IBS) errors, and 17.5% higher concordance index (C-index). Further, a continuous seizure prediction sensitivity of 83% and specificity of 87% were obtained even over a 5-min prediction horizon (the average time between successive seizure onsets was 5 min long). These results suggest that the RSF model can be used to effectively quantify the likelihood of seizure onsets over time to the patients and caregivers, promoting informed decision making.

Neuroelectric Mechanisms of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage.

Delayed cerebral ischemia (DCI) remains a challenging but very important condition, because DCI is preventable and treatable for improving functional outcomes after aneurysmal subarachnoid hemorrhage (SAH). The pathologies underlying DCI are multifactorial. Classical approaches to DCI focus exclusively on preventing and treating the reduction of blood flow supply. However, recently, glutamate-mediated neuroelectric disruptions, such as excitotoxicity, cortical spreading depolarization and seizures, and epileptiform discharges, have been reported to occur in high frequencies in association with DCI development after SAH. Each of the neuroelectric disruptions can trigger the other, which augments metabolic demand. If increased metabolic demand exceeds the impaired blood supply, the mismatch leads to relative ischemia, resulting in DCI. The neuroelectric disruption also induces inverted vasoconstrictive neurovascular coupling in compromised brain tissues after SAH, causing DCI. Although glutamates and the receptors may play central roles in the development of excitotoxicity, cortical spreading ischemia and epileptic activity-related events, more studies are needed to clarify the pathophysiology and to develop novel therapeutic strategies for preventing or treating neuroelectric disruption-related DCI after SAH. This article reviews the recent advancement in research on neuroelectric disruption after SAH.

Menthol exerts TRPM8-independent antiepileptic effects in prefrontal cortex pyramidal neurons

Menthol is a natural compound that evokes cold sensations by activating TRPM8 channels in peripheral sensory receptors. Little is known about the effects of this compound on brain neurons. It has been shown previously that menthol exerts antiepileptic effects in hippocampal neurons by enhancing GABA receptors. The aim of this patch-clamp study was to assess the effects of menthol on sodium currents, action potentials and epileptiform events in cortical neurons.Menthol inhibited fast voltage-gated sodium channels and neuronal excitability defined as the number of action potentials per depolarization step. The influence of menthol on epileptic events was also assessed in this study. Interictal epileptiform events lasting <2 s were recorded in zero magnesium high potassium proepileptic extracellular solution. The frequency of these epileptiform events was inhibited by menthol (200 µM). Ictal epileptic events lasting >100 s were recorded in zero magnesium proepileptic extracellular solution containing 4-AP. The frequency of these ictal events was potently decreased by menthol. TRPM8 channels were not involved in the inhibitory effect of menthol on ictal events because epileptic discharges persisted in the presence of the TRPM8 inhibitor AMTB. Moreover, ictal events were inhibited by therapeutic concentrations of the antiepileptic drug carbamazepine. Menthol and carbamazepine inhibited ictal events to a similar extent.This study showed that menthol exerts antiepileptic effects in cortical neurons.

Focal Electrographic Hints in Epileptic Spasms

Epileptic spasms (ES) is an age specific seizure disorder which is more frequent in infancy. Frequent untreated epileptic events have negative effect on development in ES patients so early diagnosis and appropriate treatments are key factors in management to avoid neurodevelopmental delay in survivors.

Traumatic lingual hematoma after generalized tonic–clonic seizure in a patient with an acquired coagulopathy

Oral lacerations are common complications of seizures and account for 92% of all oral injuries. Seizures are relatively commonly associated with chronic alcohol consumption. It is already known that provoked seizures can occur after a sudden cessation of prolonged alcohol intoxication. Meanwhile, chronic alcohol consumption can disrupt the blood coagulation process on several levels. This report aims to present a case of generalized tonic–clonic seizure in a man with chronic alcoholism and acquired coagulopathy who suffered severe tongue injury during a seizure. A 45-year-old man was brought to the emergency department after a first-in-life generalized tonic–clonic seizure. He gave information that he bit his tongue during the seizure. Shortly afterward, the patient had another generalized seizure during which he stopped breathing and was intubated. On admission, the patient was sedated, intubated, and on mechanical ventilation, with no signs of focal neurological deficit. A detailed physical examination revealed massive tongue swelling, which was significantly moved forward. Laboratory tests revealed coagulopathy (INR 2,10) severe thrombocytopenia with a platelet count of 50x109/L. Electrolyte values were in the reference range. According to the maxillofacial surgeon's recommendation, he was treated conservatively, and after 2 weeks, he was clinically stable with a significant reduction of lingual hematoma and without new epileptic events. In our case, decreased platelet count and probable platelet dysfunction associated with chronic alcohol consumption and tongue bite during generalized tonic–clonic seizure played a significant role in developing lingual hematoma. These fast-developing lingual hematomas can lead to possible airway obstruction; therefore, careful observation and timely intubation are mandatory to prevent possible fatal complications.

Deep Learning for Epilepsy monitoring: A survey

Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diagnostic monitoring is continuous video-electroencephalography (EEG), which ideally captures all epileptic events and dis-charges. Automated monitoring of seizures and epileptic activity from EEG would save time and resources, it is the focus of much EEG-based epilepsy research. The purpose of this paper is to provide a survey in order to understand, classify and benchmark the key parameters of deep learning-based approaches that were applied in the processing of EEG signals for epilepsy monitoring. This survey identifies the availability of data and the black-box nature of DL as the main challenges hindering the clinical acceptance of EEG analysis systems based on Deep Learning and suggests the use of Explainable Artificial Intelligence (XAI) and Transfer Learning to overcome these issues. It also underlines the need for more research to recognize the full potential of big data, Computing Edge, IoT to implement wearable devices that can assist epileptic patients and improve their quality of life.

Brain Rattled, Heart Shackled: Ictal Asystole in a Patient without Prior History of Epilepsy or Arrhythmia

We present a case of ictal asystole in an 81-year-old female, with no prior history of epileptic activity, or cardiac history suggestive of arrhythmia, who suffered several seemingly unrelated epileptic and asystolic episodes prior to finally having a witnessed seizure followed by an asystolic event. Following this event, all atrioventricular (AV) nodal blockers, and medications with potential seizure threshold lowering activity were stopped, and anti-epileptic medication was optimized. Due to the wishes of the patient’s family, no invasive interventions were pursued.However, the patient continued to be medically treated with anti-epileptic therapy and had no further asystolic events. Unfortunately, the patient’s overall clinical status deteriorated, and she subsequently passed during her hospital stay after being made do not resuscitate and do not intubate (DNR/DNI) by the family and then subsequently comfort care. Prior to her passing, however, she had remained free of epileptic events for 10 days and free of asystolic events for 21 days.