Probability Of Seizures Research Articles

Diagnostic Accuracy of the Persyst Automated Seizure Detector in the Neonatal Population.

Neonatal seizures are diagnostically challenging and predominantly electrographic-only. Multichannel video continuous electroencephalography (cEEG) is the gold standard investigation, however, out-of-hours access to neurophysiology support can be limited. Automated seizure detection algorithms (SDAs) are designed to detect changes in EEG data, translated into user-friendly seizure probability trends. The aim of this study was to evaluate the diagnostic accuracy of the Persyst neonatal SDA in an intensive care setting. Single-centre retrospective service evaluation study in neonates undergoing cEEG during intensive care admission to Great Ormond Street Hospital (GOSH) between May 2019 and December 2022. Neonates with <44 weeks corrected gestational age, who had a cEEG recording duration >60 minutes, whilst inpatient in intensive care, were included in the study. One-hour cEEG clips were created for all cases (seizures detected) and controls (seizure-free) and analysed by the Persyst neonatal SDA. Expert neurophysiology reports of the cEEG recordings were used as the gold standard for diagnostic comparison. A receiver operating characteristic (ROC) curve was created using the highest seizure probability in each recording. Optimal seizure probability thresholds for sensitivity and specificity were identified. Eligibility screening produced 49 cases, and 49 seizure-free controls. Seizure prevalence within those patients eligible for the study, was approximately 19% with 35% mortality. The most common case seizure aetiology was hypoxic ischaemic injury (35%) followed by inborn errors of metabolism (18%). The ROC area under the curve was 0.94 with optimal probability thresholds 0.4 and 0.6. Applying a threshold of 0.6, produced 80% sensitivity and 98% specificity. The Persyst neonatal SDA demonstrates high diagnostic accuracy in identifying neonatal seizures; comparable to the accuracy of the standard Persyst SDA in adult populations, other neonatal SDAs, and amplitude integrated EEG (aEEG). Overdiagnosis of seizures is a risk, particularly from cEEG recording artefact. To fully examine its clinical utility, further investigation of the Persyst neonatal SDA's accuracy is required, as well as confirming the optimal seizure probability thresholds in a larger patient cohort.

Prediction of Epilepsy Seizures by Machine Learning Methods

According to the Globe Health Organization (WHO), more than 50 million people throughout the world are living with a diagnosis of epilepsy, making it perhaps of the most widely recognized neurological issue. Epileptic seizures are a leading cause of hospitalization and mortality across the globe. Accurate and prompt diagnosis is more crucial than ever given the increase in epileptic seizures all through the globe and their effect on individuals' lives. Epilepsy, cancer, diabetes, heart disease, thyroid disease, and many more are only some of the diseases for which machine learning approaches are being applied in prediction and diagnosis. Epilepsy is one ailment that may be treated early on to save a person's life. The main objective of this research is to use feature label extraction to the dataset in order to obtain the best ML models for epileptic seizures. In order to predict epilepsy, we used the techniques of logistic regression, SVM, linear SVM, KNN, and RNN in this study. The models employed in this research are accurate to varying degrees and have attributes including precision, recall, f1-score, and support. This study demonstrates that the model is able to accurately predict the occurrence of epilepsy. Our discoveries demonstrate that involving Examination highlight extraction in the dataset, the Regional Neural Network (RNN) model with 99.9998 % Training data accuracy and 97.78% Test data accuracy and 100% prediction probability of epilepsy seizure produces the best results and also the feature characteristics of RNN is better as compared to other models used in current research work.

External validation and comparison of clinical scores for predicting late seizures after intracerebral hemorrhage in Chinese patients

PurposeClinical scores have been established to predict the probability of late seizures following intracerebral hemorrhage (ICH) for individual patients, including the CAVE, CAVS and LANE scores. The purpose of this study was to compare these prediction scores in the Chinese population and undertake an independent external validation on them. MethodsAt one tertiary hospital in China, we retrospectively recruited consecutive inpatients who had been diagnosed with ICH. Medical records and tele interviews with a modified standardized questionnaire were used to identify late seizures. All the predictors of the prediction scores were collected from patient charts and databases by a standardized data collection protocol. The external validation of the prediction scores was quantified by the area under the curve (AUC), sensitivity, specificity, Youden index (YI), positive predictive value (PPV), and negative predictive value (NPV). Results69 (5.4%) of 1276 patients experienced late seizures after ICH. There was no significant difference in the CAVE, CAVS, and LANE scores, which had AUCs of 0.75 (95% CI = 0.70–0.81), 0.74 (95% CI = 0.68–0.80), and 0.76 (95% CI = 0.70–0.82), respectively. At the optimal cutoff score, the LANE score had a lower sensitivity but a higher specificity than the CAVE and CAVS scores. Among the three prediction scores, the LANE score had a higher PPV than the others (0.145 vs. 0.088, 0.083), while the NPV was similar among the three prediction scores (0.989, 0.989, and 0.972). ConclusionOur study showed that the CAVE, CAVS and LANE scores had similar AUCs for the occurrence of late seizures, but the LANE score had a relatively high PPV at the optimal cutoff score. Due to low evidence for using prophylactic antiseizure medications (ASM) in patients with ICH and poor availability of specialist stroke care in China, the LANE score with a cutoff score of 3 could be an applicable prediction tool in Chinese patients with ICH.

Subclinical Epileptiform Activity in Dementia with Lewy Bodies.

Patients with dementia with Lewy bodies (DLB) have a higher probability of seizures than in normal aging and in other types of neurodegenerative disorders. Depositions of α-synuclein, a pathological hallmark of DLB, can induce network excitability, which can escalate into seizure activity. Indicator of seizures are epileptiform discharges as observed using electroencephalography (EEG). However, no studies have so far investigated the occurrence of interictal epileptiform discharges (IED) in patients with DLB. To investigate if IED as measured with ear-EEG occurs with a higher frequency in patients with DLB compared to healthy controls (HC). In this longitudinal observational exploratory study, 10 patients with DLB and 15 HC were included in the analysis. Patients with DLB underwent up to three ear-EEG recordings, each lasting up to 2 days, over a period of 6 months. At baseline, IED were detected in 80% of patients with DLB and in 46.7% of HC. The spike frequency (spikes or sharp waves/24 hours) was significantly higher in patients with DLB as compared to HC with a risk ratio of 2.52 (CI, 1.42-4.61; P-value = 0.001). Most IED occurred at night. Long-term outpatient ear-EEG monitoring detects IED in most patients with DLB with an increased spike frequency compared to HC. This study extends the spectrum of neurodegenerative disorders in which epileptiform discharges occurs at an elevated frequency. It is possible that epileptiform discharges are, therefore, a consequence of neurodegeneration. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Best-worst scaling preferences among patients with well-controlled epilepsy: Pilot results.

Epilepsy is a common, serious condition. Fortunately, seizure risk decreases with increasing seizure-free time on antiseizure medications (ASMs). Eventually, patients may consider whether to stop ASMs, which requires weighing treatment benefit versus burden. We developed a questionnaire to quantify patient preferences relevant to ASM decision-making. Respondents rated how concerning they would finding relevant items (e.g., seizure risks, side effects, cost) on a Visual Analogue Scale (VAS, 0-100) and then repeatedly chose the most and least concerning item from subsets (best-worst scaling, BWS). We pretested with neurologists, then recruited adults with epilepsy who were seizure-free at least one year. Primary outcomes were recruitment rate, and qualitative and Likert-based feedback. Secondary outcomes included VAS ratings and best-minus-worst scores. Thirty-one of 60 (52%) contacted patients completed the study. Most patients felt VAS questions were clear (28; 90%), easy to use (27; 87%), and assessed preferences well (25; 83%). Corresponding results for BWS questions were 27 (87%), 29 (97%), and 23 (77%). Physicians suggested adding a 'warmup' question showing a completed example and simplifying terminology. Patients suggested ways to clarify instructions. Cost, inconvenience of taking medication, and laboratory monitoring were the least concerning items. Cognitive side effects and a 50% seizure risk in the next year were the most concerning items. Twelve (39%) of patients made at least one 'inconsistent choice' for example ranking a higher seizure risk as lower concern compared with a lower seizure risk, though 'inconsistent choices' represented only 3% of all question blocks. Our recruitment rate was favorable, most patients agreed the survey was clear, and we describe areas for improvement. 'Inconsistent' responses may lead us to collapse seizure probability items into a single 'seizure' category. Evidence regarding how patients weigh benefits and harms may inform care and guideline development.

Antiseizure Medication Withdrawal Practice Patterns: A Survey Among Members of the American Academy of Neurology and EpiCARE.

To describe neurologist practice patterns, challenges, and decision support needs pertaining to withdrawal of antiseizure medications (ASMs) in patients with well-controlled epilepsy. We sent an electronic survey to (1) US and (2) European physician members of the American Academy of Neurology and (3) members of EpiCARE, a European Reference Network for rare and complex epilepsies. Analyses included frequencies and percentages, and we showed distributions through histograms and violin plots. We sent the survey to 4,923 individuals; 463 consented, 411 passed eligibility questions, and 287 responded to at least 1 of these questions. Most respondents indicated that they might ever consider ASM withdrawal, with respondents treating mostly children being more likely ever to consider withdrawal (e.g., medical monotherapy: children 96% vs adults 81%; p < 0.05). The most important factors when making decisions included seizure probability (83%), consequences of seizures (73%), and driving (74%). The top challenges when making decisions included unclear seizure probability (81%), inadequate guidelines (50%), and difficulty communicating probabilities (45%). Respondents would consider withdrawal after a median of 2-year seizure freedom, but also responded that they would begin withdrawal on average only when the postwithdrawal seizure relapse risk in the coming 2 years was less than 15%-30%. Wide variation existed in the use of words or numbers in respondents' counsel methods, for example, percentages vs frequencies or probability of seizure freedom vs seizure. The most highly rated point-of-care methods to inform providers of calculated risk were Kaplan-Meier curves and showing percentages only, rather than pictographs or text recommendations alone. Most surveyed neurologists would consider withdrawing ASMs in seizure-free individuals. Seizure probability was the largest factor driving decisions, yet estimating seizure probabilities was the greatest challenge. Respondents on average indicated that they may withdraw ASM after a minimum seizure-free duration of 2 years, yet also on average were willing to withdraw when seizure risk decreased below 15%-30%, which is lower than most patients' postwithdrawal risk at 2-year seizure freedom and lower than the equivalent even of a first seizure of life. These findings will inform future efforts at developing decision support tools aimed at optimizing ASM withdrawal decisions.

CystiHuman: A model of human neurocysticercosis.

IntroductionThe Taenia solium tapeworm is responsible for cysticercosis, a neglected tropical disease presenting as larvae in the body of a host following taenia egg ingestion. Neurocysticercosis (NCC), the name of the disease when it affects the human central nervous system, is a major cause of epilepsy in developing countries, and can also cause intracranial hypertension, hydrocephalus and death. Simulation models can help identify the most cost-effective interventions before their implementation. Modelling NCC should enable the comparison of a broad range of interventions, from treatment of human taeniasis (presence of an adult taenia worm in the human intestine) to NCC mitigation. It also allows a focus on the actual impact of the disease, rather than using proxies as is the case for other models.MethodsThis agent-based model is the first model that simulates human NCC and associated pathologies. It uses the output of another model, CystiAgent, which simulates the evolution of pig cysticercosis and human taeniasis, adding human and cyst agents, including a model of cyst location and stage, human symptoms, and treatment. CystiHuman also accounts for delays in the appearance of NCC-related symptoms. It comprises three modules detailing cyst development, seizure probability and timing, and intracranial hypertension/hydrocephalus, respectively. It has been implemented in Java MASON and calibrated in three endemic villages in Peru, then applied to another village (Rica Playa) to compare simulation results with field data in that village.Results and discussionDespite limitations in available field data, parameter values found through calibration are plausible and simulated outcomes in Rica Playa are close to actual values for NCC prevalence and the way it increases with age and cases with single lesions. Initial simulations further suggest that short-term interventions followed by a rapid increase in taeniasis prevalence back to original levels may have limited impacts on NCC prevalence.

Eliciting neural mechanisms of music medicine for epilepsy

ABSTRACT Prior research involving persons with drug-resistant epilepsy has demonstrated that listening to some music decreases the probability of clinical seizures and their related comorbidities. This article reviews recent research designed to elicit the neural mechanisms behind positive outcomes on biomarkers of the disease. Using novel music analytical and neurophysiological experimental methods, our results showed positive effects on epilepsy using 15-second gamma-band (40-Hz) complex tones as well as Mozart's Sonata for Two Pianos in D Major (K448). We also observed greater effects with increased stimulus duration. Further analysis elicited effects localized to bilateral frontal brain regions due to transitions between musical phrases. Finally, music matched for patient preference from a range of musical styles was not as effective as 40-Hz tones or Mozart's K448. Understanding these results required expertise in both music and neuroscience, and could yield reliable music-based interventions for epilepsy that may also be transferable to other brain disorders.

Assessing efficacy of endoscopic ventriculocisternostomy using a semi-rigid needle endoscope in сhildren with hydrocephalus and concomitant drug-resistant epilepsy: results of CT brain perfusion and time-frequency EEG analysis

Background. The probability of seizures after endoscopic surgical treatment of hydrocephalus may comprise up to 9.5%, therefore accounting for a need to find solutions for alleviating the surgical trauma to the brain. One option to this problem might be based on using “seamless” neurosurgery, particularly endoscopes with a minimal outer sheath diameter (needle endoscopes, shunt endoscopes).Objective: to evaluate major outcomes of surgically treated epilepsy, cerebral haemodynamics and electroencephalogram (EEG) in children with hydrocephalus and concomitant drug-resistant epilepsy before and after endoscopic ventriculocysternostomy of the third ventricular floor by using a semi-rigid needle endoscope.Material and methods. In the early and remote postoperative periods, no epileptic seizures were observed in 57.1% of subjects (Engel Class I). Mean seizure frequency decreased from 12.9±6.1 to 0.82±0.31 per month (p&lt;0.01). A significant decline in slow-wave amplitude and the paroxysmal index from 19.1±2.5% to 6.9±1.7% (p&lt;0.01) were observed, as well as improvement of cerebral hemodynamics in the form of increased mean cerebral blood flow by 29.1±4.3% (p&lt;0.01), mean circulating blood volume by 22.4±5.27% (p&lt;0.05) and decreased mean transit time by 12.8±2.5% (p&lt;0.05).Results. In the early and remote postoperative periods, no epileptic seizures were observed in 57.1% of subjects (Engel Class I). Mean seizure frequency decreased from 12.9±6.1 to 0.82±0.31 per month (p&lt;0.01). A significant decline in slow-wave amplitude and the paroxysmal index from 19.1±2.5% to 6.9±1.7% (p&gt;&lt;0.01) were observed, as well as improvement of cerebral hemodynamics in the form of increased mean cerebral blood flow by 29.1±4.3% (p&gt;&lt;0.01), mean circulating blood volume by &gt;&lt;0.01). A significant decline in slow-wave amplitude and the paroxysmal index from 19.1±2.5% to 6.9±1.7% (p&lt;0.01), mean circulating blood volume by 22.4±5.27% (p&lt;0.05) and decreased mean transit time by 12.8±2.5% (p&gt;&lt;0.05).

Demographic and Clinical Characteristics of Patients Who Died of Methanol Toxicity During COVID 19 Period in Loghman-e Hakim Hospital in Tehran

Background: Outbreaks of methanol poisoning were observed during the COVID-19 pandemic. Acute methanol poisoning is a global crisis. Methanol can cause acute and fatal toxicity through metabolic acidosis. In the present study, we evaluated demographic, clinical, and paraclinical characteristics of patients who died in the recent outbreak of methanol poisoning in Tehran from March to April 2020. Methods: This cross-sectional study was accomplished at the Loghman-Hakim Hospital in Tehran on 80 patients who died of methanol toxicity. Demographic, clinical, and laboratory data were collected retrospectively from the patient’s files and analyzed with appropriate statistical tests. Results: Men were significantly more involved than women (%85 vs. %15). There were no significant differences between other characteristics of male and female patients, including the time between consumption to arrive hospital, dialysis sessions, pulse rate, respiratory rate, loss of consciousness, seizure, acute kidney injury, brain CT, and Intracerebral Hemorrhage ( ICH). Blood sugar, serum potassium, and liver function tests were higher than average in most of the patients. Conclusion: Our study showed that this outbreak of methanol poisoning was due to the use of alcoholic drinks that contain methanol. Men were primarily affected that could be because of the cultural and social status of our country. The greater seizure probability in females could be because of enhancing the NMDA receptor by estrogen. Abnormalities in Alanine aminotransferase (ALT), Aspartate Aminotransferase (AST), and Prothrombin Time (PT) were seen in most patients, indicating liver damage. Misbeliefs about the protective effects of alcohol consumption against COVID-19 may lead many to consume poorly made alcohols that contain methanol and outbreaks of methanol intoxication.

Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology.

Liver cirrhosis is a clinical chronic developmental liver disease, which is caused by long-term or repeated effects of liver dysfunction, and there are more and more cases of epileptic seizures in patients with liver cirrhosis and HEV infection. This article aims to study how to analyze epileptic seizures in patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology. This article proposes a deep learning neural network algorithm based on deep multimodal fusion technology, and how to use this algorithm to automatically detect and classify epileptic seizures. The data in the experiment in this article show that the prevalence of epilepsy accounts for 1% of the world's population, about 56.7 million people, and 1 in 25 people may have an epileptic seizure at some time in their lives, and in each person's life, the probability of seizures due to various reasons is 10%. In 2016, the proportion of males with cirrhosis reached 16%, females reached 8%, and males were 8% higher than females, which is a full double. The test results show that with the increase in patients with cirrhosis and overlapping HEV infection, the frequency of epileptic seizures is also getting higher and higher, indicating that the frequency of epileptic seizures has been increased in patients with cirrhosis and overlapping HEV infection. Therefore, it is imperative to analyze the epileptic seizures of patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology.

Demographic and Clinical Characteristics of Patients Who Died of Methanol Toxicity During COVID 19 Period in Loghman-e Hakim Hospital in Tehran

Background: Outbreaks of methanol poisoning were observed during the Covid 19 pandemic. Acute methanol poisoning is a global crisis. Methanol can cause acute and fatal toxicity through metabolic acidosis. In the present study, we evaluate demographic, clinical, and paraclinical characteristics of patients who died in the recent outbreak of methanol poisoning in Tehran in March and April 2020. Methods: This is a cross-sectional study that was accomplished at the Loghman-Hakim Hospital in Tehran that performed on 80 patients who died of methanol toxicity; demographic, clinical, and laboratory data were collected retrospectively from the patient’s files and analyzed with appropriate statistical tests. Results: Men were significantly more involved than women %85 vs. %15. There were no significant differences between other characteristics of male and female patients, including time between consumption to arrive hospital, dialysis sessions, pulse rate, respiratory rate, loss of consciousness, seizure, acute kidney injury, brain C.T. and ICH (intracerebral hemorrhage). BS (blood sugar) and liver function tests and serum potassium were higher than average in most of the patients. Discussion and conclusion: Our study shows this outbreak of methanol poisoning was due to the use of alcoholic drinks that contain methanol. Men were primarily affected that could be because of the cultural and social status of our country. The greater seizure probability in females could be because of enhancing the NMDA receptor by estrogen. Abnormality in ALT (alanine aminotransferase), AST (aspartate aminotransferase), and PT (prothrombin time) was seen in most patients, indicating liver damage. Misbeliefs about the protective effects of alcohol consumption against COVID-19 may lead many to consume poorly made alcohols that contain methanol and outbreaks of methanol intoxication.

Factors Predicting the Frequency of Second Seizures in Patients Presenting to the Emergency Department With Seizures: A Prospective Observational Study.

IntroductionThis study aimed to determine the factors that trigger seizures in patients reporting to our emergency department (ED) with seizures and the factors that affect recurrent seizures during the emergency department stay.Materials and methodsThis study was designed prospectively and was conducted among patients over the age of 18 years who reported to the ED of the Education and Research Hospital with complaints of epileptic seizure between July 01, 2020 and July 01, 2021. In addition to the sociodemographic information of the patients, the time of admission after the seizure, the medications used, comorbidities, the treatment given in the ED, history of trauma, previous epilepsy diagnosis, time of last seizure, alcohol use in the last 24 hours, insomnia, presence of infective symptoms in the past week, vital parameters, blood tests, and presence of recurrent seizure during hospital follow-up were recorded.ResultsThe median age of the 102 patients included in the statistical analysis was 37 (25%-57%), and 61 (59.8%) were men. Patients who came to the ED with the complaint of seizures were divided into two groups, namely, those who had recurrent seizures and those who did not. When the differences between the groups in terms of various variables were examined, no statistically significant difference was found in the univariate analysis, except for WBC, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) values. The diagnostic value of WBC, AST, and ALT levels in predicting recurrent seizures in emergency follow-up was analyzed using a receiver operating characteristic curve.ConclusionIn this study, we could not find a parameter that can predict the probability of recurrent seizures in the ED in patients presenting with epileptic seizures.

Seizure Incidence Rates in Children and Adults With Familial Cerebral Cavernous Malformations.

Seizure incidence rates related to familial cerebral cavernous malformation (FCCM) are not well described, especially for children. To measure the seizure incidence rate, examine seizure predictors, and characterize epilepsy severity, we studied a cohort of children and adults with FCCM enrolled in the Brain Vascular Malformation Consortium (BVMC). Seizure data were collected from participants with FCCM in the BVMC at enrollment and during follow-up. We estimated seizure probability by age and tested whether cerebral cavernous malformation (CCM) counts or genotype were associated with earlier seizure onset. The study cohort included 479 FCCM cases. Median age at enrollment was 42.5 years (interquartile range 22.5-55.0) and 19% were children (<18 years old). Median large CCM count was 3 (interquartile range 1-5). Among 393 with genotyping, mutations were as follows: CCM1 (Common Hispanic Mutation) (88%), another CCM1 mutation (5%), CCM2 mutations (5%), and CCM3 mutations (2%). Prior to or during the study, 202 (42%) had a seizure. The cumulative incidence of a childhood seizure was 20.3% (95% confidence interval [CI] 17.0-23.4) and by age 80 years was 60.4% (95% CI 54.2-65.7). More total CCMs (hazard ratio [HR] 1.24 per SD unit increase, 95% CI 1.1-1.4) or more large CCMs (HR 1.5 per SD unit increase, 95% CI 1.2-1.9) than expected for age and sex increased seizure risk. A CCM3 mutation also increased risk compared to other mutations (HR 3.11, 95% CI 1.15-8.45). Individuals with a seizure prior to enrollment had increased hospitalization rates during follow-up (incidence rate ratio 10.9, 95% CI 2.41-49.32) compared to patients without a seizure history. Individuals with FCCM have a high seizure incidence and those with more CCMs or CCM3 genotype are at greater risk. Seizures increase health care utilization in FCCM.

Efficacy of Home Anticonvulsant Administration for Second-Line Status Epilepticus Treatment

To investigate whether receiving a second-line anticonvulsant medication that is part of a patient's home regimen influences outcomes in benzodiazepine-refractory convulsive status epilepticus. Using the Established Status Epilepticus Treatment Trial data, allocation to a study drug included in the patient's home anticonvulsant medication regimen was compared to receipt of an alternative second-line study medication. The primary outcome was cessation of clinical seizures with improved consciousness by 60 minutes after study drug initiation. Secondary outcomes were seizure cessation adjudicated from medical records and adverse events. We performed inverse probability of treatment-weighted (IPTW) logistic regressions. Of 462 patients, 232 (50%) were taking 1-2 of the 3 study medications at home. The primary outcome was observed in 39/89 (44%) patients allocated to their home medication vs 76/143 (53%) allocated to a nonhome medication (IPTW odds ratio [OR] 0.66, 95% confidence interval [CI] 0.39-1.14). The adjudicated outcome occurred in 37/89 (42%) patients vs 82/143 (57%), respectively (IPTW OR 0.52, 95% CI 0.30-0.89). There was no interaction between study levetiracetam and home levetiracetam and there were no differences in adverse events. There was no difference in the primary outcome for patients who received a home medication vs nonhome medication. However, the retrospective evaluation suggested an association between receiving a nonhome medication and seizure cessation. This study provides Class II evidence that for patients with refractory convulsive status epilepticus, use of a home second-line anticonvulsant compared to a nonhome anticonvulsant did not significantly affect the probability of stopping seizures.

Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation.

BackgroundWhile the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management.MethodsTo explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories.FindingsSeizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep.InterpretationOur results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.

Long-term seizure dynamics are determined by the nature of seizures and the mutual interactions between them

The seemingly random and unpredictable nature of seizures is a major debilitating factor for people with epilepsy. An increasing body of evidence demonstrates that the epileptic brain exhibits long-term fluctuations in seizure susceptibility, and seizure emergence seems to be a consequence of processes operating over multiple temporal scales. A deeper insight into the mechanisms responsible for long-term seizure fluctuations may provide important information for understanding the complex nature of seizure genesis. In this study, we explored the long-term dynamics of seizures in the tetanus toxin model of temporal lobe epilepsy. The results demonstrate the existence of long-term fluctuations in seizure probability, where seizures form clusters in time and are then followed by seizure-free periods. Within each cluster, seizure distribution is non-Poissonian, as demonstrated by the progressively increasing inter-seizure interval (ISI), which marks the approaching cluster termination. The lengthening of ISIs is paralleled by: increasing behavioral seizure severity, the occurrence of convulsive seizures, recruitment of extra-hippocampal structures and the spread of electrographic epileptiform activity outside of the limbic system. The results suggest that repeated non-convulsive seizures obey the ‘seizures-beget-seizures’ principle, leading to the occurrence of convulsive seizures, which decrease the probability of a subsequent seizure and, thus, increase the following ISI. The cumulative effect of repeated convulsive seizures leads to cluster termination, followed by a long inter-cluster period. We propose that seizures themselves are an endogenous factor that contributes to long-term fluctuations in seizure susceptibility and their mutual interaction determines the future evolution of disease activity.

Spatio-temporal heterogeneity in hippocampal metabolism in control and epilepsy conditions

The hippocampus's dorsal and ventral parts are involved in different operative circuits, the functions of which vary in time during the night and day cycle. These functions are altered in epilepsy. Since energy production is tailored to function, we hypothesized that energy production would be space- and time-dependent in the hippocampus and that such an organizing principle would be modified in epilepsy. Using metabolic imaging and metabolite sensing ex vivo, we show that the ventral hippocampus favors aerobic glycolysis over oxidative phosphorylation as compared to the dorsal part in the morning in control mice. In the afternoon, aerobic glycolysis is decreased and oxidative phosphorylation increased. In the dorsal hippocampus, the metabolic activity varies less between these two times but is weaker than in the ventral. Thus, the energy metabolism is different along the dorsoventral axis and changes as a function of time in control mice. In an experimental model of epilepsy, we find a large alteration of such spatiotemporal organization. In addition to a general hypometabolic state, the dorsoventral difference disappears in the morning, when seizure probability is low. In the afternoon, when seizure probability is high, the aerobic glycolysis is enhanced in both parts, the increase being stronger in the ventral area. We suggest that energy metabolism is tailored to the functions performed by brain networks, which vary over time. In pathological conditions, the alterations of these general rules may contribute to network dysfunctions.

A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data.

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.

ScoreNet: A Neural Network-Based Post-Processing Model for Identifying Epileptic Seizure Onset and Offset in EEGs.

We design an algorithm to automatically detect epileptic seizure onsets and offsets from scalp electroencephalograms (EEGs). The proposed scheme consists of two sequential steps: detecting seizure episodes from long EEG recordings, and determining seizure onsets and offsets of the detected episodes. We introduce a neural network-based model called ScoreNet to carry out the second step by better predicting the seizure probability of pre-detected seizure epochs to determine seizure onsets and offsets. A cost function called log-dice loss with a similar meaning to the F1 score is proposed to handle the natural data imbalance inherent in EEG signals signifying seizure events. ScoreNet is then verified on the CHB-MIT Scalp EEG database in combination with several classifiers including random forest, convolutional neural network (CNN), and logistic regression. As a result, ScoreNet improves seizure detection performance over lone epoch-based seizure classification methods; F1 scores increase significantly from 16-37% to 53-70%, and false positive rates per hour decrease from 0.53-5.24 to 0.05-0.61. This method provides clinically acceptable latencies of detecting seizure onset and offset of less than 10 seconds. In addition, an effective latency index is proposed as a metric for detection latency whose scoring considers undetected events to provide better insight into onset and offset detection than conventional time-based metrics.