DIAGNOSTIC PERFORMANCE OF THE AMBULATORY EEG VERSUS ROUTINE EEG AND RISK FACTORS FOR SEIZURE RECURRENCE AMONG INDIVIDUALS WITH FIRST SINGLE UNPROVOKED SEIZURES

Background and objectives: Routine electroencephalography (rEEG) remains central in the prognosis of seizure recurrence among individuals with a First Single Unprovoked seizure (FSUS). Furthermore, it is well-established that the presence of epileptiform discharge (ED) in the EEG increases the risk of further seizures among individuals with FSUS up to 3 times compared with individuals without such EEG changes. However, the rEEG has low sensitivity, leaving patients and clinicians without a fast and accurate tool for the prognosis of further seizures. This study aims to determine and compare the discriminative power, clinical predictive value, and global diagnostic accuracy of the ambulatory EEG compared with the first rEEG and second rEEG. This study also aims to determine risk factors for further seizures among individuals with FSUS, including ED in the ambulatory EEG. Methods: The study used a prospective cohort design with a total of 100 individuals with FSUS who underwent three modalities of EEG (first rEEG, second rEEG and Ambulatory EEG) and who were followed up for one year period. All the required information was available in this dataset, and further seizures were prospectively recorded. The three EEG (first, second rEEGs and ambulatory EEG) were interpreted by licensed neurologists recognized by the Royal College of Physicians and Surgeons of Canada and fully accredited by the Canadian Society of Clinical Neurophysiologists. Diagnosis of epilepsy was made based on clinical, neurophysiology and imaging tests following the definition of epilepsy by the International League Against Epilepsy 2014. Receiver-operating-characteristic (ROC) analysis was used to evaluate the results. Also, P a g e iii table-life and survival analysis were used to determine the risk for further seizures during the 52 weeks follow-up period. Results: We found that the ambulatory EEG’s diagnostic accuracy was better than the first and second EEG (0.79 vs. 0.51 and 0.54, respectively) in the population. Age group was a confounder in the association between seizure recurrence at 52 weeks and the presence of ED in the ambulatory EEG. The presence of ED in the ambulatory EEG increased the risk of seizure recurrence among individuals with FSUS 3.2 times when adjusted for use of antiseizure medication (ASM) and age group. Finally, other risk factors modifying the association between further seizures and the presence of ED in the ambulatory EEG included age group of >60 years (HR: 0.27 95%CI: 0.10,0.74) and the use of ASM (HR: 12.9, 95%CI: 5.6, 29.3). Conclusions: The overall diagnostic accuracy of the ambulatory EEG as a means of detecting ED among individuals with FSUS is better than the first and second rEEG. Furthermore, ED in the ambulatory EEG is a significant risk factor predicting further seizures after a single unprovoked seizure after adjusting for the use of ASM and age group. Significance: This study advanced our knowledge about the use of ambulatory EEG as an ancillary tool for predicting further seizures after FSUS and established that the presence of epileptiform activity in the ambulatory EEG is a risk factor for further seizures after adjusting for use of ASM and age group. The use of ambulatory EEG may reduce diagnostic errors and is also low-cost and better tool which can be used worldwide for more accurate diagnosis of epilepsy compared to rEEG

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.Fil: Antonio Quintero, Rincón. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Pereyra, Marcelo Fabián. University of Bristol; Reino UnidoFil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Batatia, Hadj. Instituto Polytechnique de Toulouse; Francia. University of Toulouse; FranciaFil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data

Electroencephalogram data platform for application of reduction methods

Long-term electroencephalogram (EEG) monitoring (≥24-h) is a resourceful tool for properly diagnosis sparse life-threatening events like non-convulsive seizures and status epilepticus in Intensive Care Unit (ICU) inpatients. Such EEG data requires objective methods for data reduction, transmission and analysis. This work aims to assess specificity and sensibility of HaEEG and aEEG methods in combination with conventional multichannel EEG when achieving seizure detection. A database architecture was designed to handle the interoperability, processing, and analysis of EEG data. Using data from CHB-MIT public EEG database, the reduced signal was obtained by EEG envelope segmentation, with 10 and 90 percentiles obtained for each segment. The use of asymmetrical filtering (2-15 Hz) and overall clinical band (1-70 Hz) was compared. The upper and lower margins of compressed segments were used to classify ictal and non-ictal epochs. Such classification was compared with the corresponding specialist seizure annotation for each patient. The difference between medians of instantaneous frequencies of ictal and non-ictal periods were assessed using Wilcoxon Rank Sum Test, which was significant for signals filtered from 2 to 15 Hz (p = 0.0055) but not for signals filtered from 1 to 70 Hz (p = 0.1816).O eletroencefalograma (EEG) de longa duração (≥24-h) em monitoramento contínuo é diferencial no diagnóstico e classificação de eventos epileptiformes, como crises não convulsivas e status epilepticus, em pacientes de Unidades de Tratamento Intensivo (UTI). Este exame requer métodos objetivos de análise, redução e transmissão de dados. O objetivo desse trabalho é avaliar a especificidade e a sensibilidade dos métodos HaEEG e aEEG em combinação com EEG multicanal convencional na detecção de eventos epileptiformes. Uma arquitetura de integração de dados foi projetada para gerir o armazenamento, processamento e análise de dados de EEG. Foram utilizados dados do banco de dados de EEG público do CHB-MIT. O sinal reduzido foi obtido pela segmentação do envelope do EEG, com percentis 10 e 90 obtidos para cada segmento. A aplicação do filtro assimétrico (2-15 Hz) e em bandas clínicas (1-70 Hz) foi comparada. Os limiares superiores e inferiores dos segmentos do aEEG e HaEEG foram usados para classificar épocas ictais e não ictais. A classificação foi comparada com as anotações feitas por um especialista para cada paciente. As medianas das frequências instantâneas para períodos ictais e não ictais foram analisadas com Wilcoxon Rank Sum Test com significância para filtragem assimétrica (p = 0,0055), mas não nas bandas clínicas (p = 0,1816)

The EEG in acute ischaemic cerebrovascular disease

The electroencephalogram (EEG) is a neurophysiological technique with high temporal resolution and sensibility in the evaluation of brain function in real time. Besides this, EEG is the gold standard for the identification of epileptogenesis and ictogenesis biomarkers. Epileptic seizures and Cerebrovascular disease are two of the most frequent neurological disorders imposing important mutual challenges. Furthermore, in recent years, stroke care has evolved remarkably and, facing a new paradigm of acute standard of care (centred on multidisciplinary Stroke Units), epileptic seizures (as stroke complications) deserve to be rethought. The EEG is an essential neurophysiological exam in the evaluation of patients with epileptic seizures, status epilepticus and/or epilepsy, both for diagnosis and classification, as well as for the establishment of a correct treatment or outcome prediction. Furthermore, EEG has been previously used in cerebrovascular disease with different purposes. However, its clinical usefulness in the differential diagnosis of transient neurological symptoms, specifically in the differentiation between a transient ischaemic attack and some epileptic seizures, and also in the diagnosis or prediction of post-stroke seizures or in post-stroke prognosis prediction, remains uncertain. In this work, we aim to use the clinical model of acute ischaemic cerebrovascular disease to study the value of EEG in the differential diagnosis of transient neurological symptoms, in the diagnosis and prediction of post-stroke seizures and epilepsy, as well as to analyse if electroencephalographic abnormalities and/or epileptic seizures are independent predictors of an anterior circulation ischaemic stroke outcome. Furthermore, since the gold standard of acute stroke care (namely intravenous alteplase treatment) is associated with a reduction of mortality and incapacity of treated patients with possible consequences in post-stroke seizure frequency, but a pro-convulsive and an epileptogenic effect of alteplase has also been described, we aim to test the hypothesis that ischaemic stroke patients treated with intravenous alteplase have a different frequency of epileptic (clinic and/or electroencephalographic) manifestations compared to non-treated patients. Different research methodologies were used in this thesis. A systematic review and meta-analysis of observational studies was performed to evaluate both the frequency of post-stroke (ictal and interictal) epileptiform activity in the EEG, and the quality of studies about this subject. Furthermore, different types of observational studies (including clinical case report, case series and cohort studies) were completed to answer relevant clinical questions. We performed a prospective longitudinal study of possible transient ischaemic attacks (TIA) patients evaluated at a tertiary centre during 36 months, with 1-3 months follow-up and also of acute anterior circulation ischaemic stroke patients, consecutively admitted to a Stroke Unit over 24 months and followed-up for one year. In both studies, patients underwent standardized clinical, diagnostic and neurophysiological assessment. A short duration (≤60 minutes) video-EEG protocol with an extended montage including 64 EEG, two electrooculogram, one electrocardiogram and at least one electromyogram channel was established. Different electroencephalographic investigation technics including visual, back-average and quantitative analysis were used in the clinical workup of patients with possible and definite, transient and established, cerebrovascular disease as tools for the differential diagnosis and for brain functional assessment, concerning not only epileptic manifestations detection and prediction but also to search for predictors of ischaemic stroke functional outcome and vital prognosis. Although epileptic seizures were the most frequent defined final diagnosis (n=13; 16.3%) in our series of 80 patients with difficult-to-diagnose transient neurological symptoms, visual inspection of EEG supported this diagnosis only in 7.5% (n=6) of patients with possible TIA. Moreover, the majority (n=6; 53.8%) of patients with the final diagnosis of epileptic seizures did not have interictal epileptiform activity in an early EEG. Furthermore, early focal slow wave activity, the most frequent EEG abnormality in this patient’s series, did not distinguished between TIA and seizure patients. Our systematic review and random-effects meta-analysis showed that the pooled frequency of post-stroke ictal and interictal epileptiform activity was 7% (95%CI: 3%-12%) and 8% (95%CI: 4%-13%) respectively. Only 2 out of 17 included studies (11.7%) attained the maximum quality score. Moreover, no study exclusively enrolled ischaemic stroke patients, highlighting the need for higher quality studies in the evaluation of epileptiform activity frequency in this type of cerebrovascular disease. Furthermore, due to detection bias, it was not possible to correlate clinical and electrographic seizures. In our prospective cohort of 151 anterior circulation acute stroke patients, we identified different post-stroke, clinical and electroencephalographic, epileptic manifestations including 22.7% (5/22) of acute symptomatic seizures that were exclusively electrographic and therefore could not otherwise be recognised. Furthermore, only EEG back-average analysis allowed the diagnosis of cortical myoclonus during intravenous alteplase perfusion in one clinical vignette included in this work and the recognition of epilepsia partialis continua as a chronic complication of this stroke type in 1.7% of patients. This original work also showed that studied clinical and EEG epileptic manifestations were not significantly different between intravenous alteplase treated and non-treated patients. This thesis work established which abnormalities of an early EEG after acute stroke (background activity asymmetry and the presence of interictal epileptiform activity) are independent predictors of epilepsy in the year after (even when adjusted for clinical and imaging stroke severity). Besides this, early (within the first 72h) post-stroke EEG features, extracted from visual (background activity diffuse slowing and asymmetry) and quantitative (such as delta-theta to alpha-beta ratio and alfa relative power) analysis were recognized as independent predictors of death or functional dependency, at hospital discharge and at 12 months after stroke. Furthermore, outcome models that incorporate delta-theta to alpha-beta ratio or alpha relative power were better than models based exclusively on clinical and imaging-related ischaemic stroke severity at hospital admission. Additionally, post-stroke acute symptomatic seizures and epilepsy were independently associated to death and to an unfavourable outcome 1 year after an acute anterior circulation ischaemic stroke, respectively. Globally, these research projects have shown the value of EEG in the current paradigm of stroke patient’s care. Furthermore, they expand the knowledge both about the EEG role as a complementary neurophysiological tool in general Neurology and about different aspects of the diagnosis and outcome of two of the most prevalent neurological disorders, Cerebrovascular Diseases and Epilepsy, in particular. Beyond the value of specific results, with this work several other research questions about EEG and seizures in ischaemic cerebrovascular disease emerge. Therefore, new possibilities of future research, ideally multicentric, clinical or translational arise

Effects of Cerebral Hypothermia on Cerebral Vasculature and Neuronal Activity During Pharmacologically-Induced Neonatal Seizures

Seizures, a common form of neurological dysfunction in newborns, occur when large groups of neurons fire in a synchronous and excessive manner. During seizures, cerebral vessels dilate to match excessive neuronal activation via increased heart rate, blood pressure, and cerebral blood flow. We hypothesized that cerebral hypothermia has beneficial neuronal, cerebrovascular, and systemic effects in neonatal seizures. Bicuculline, a GABAA receptor blocker, produces sustained seizure activity in neonatal piglets for over two hours. Electrocorticogram recordings provided no evidence that excessive neuronal activation or cerebral vasodilation were mitigated by hypothermia. These novel data suggest that cerebral hypothermia has no anticonvulsant effects and does not prevent the cerebral blood flow increase in neonatal seizures. Head cooling greatly reduced ictal tachycardia and attenuated blood pressure responses, indicating potential systemic benefits of cerebral hypothermia. Collectively, this study shows promise for the therapeutic capabilities of cerebral hypothermia during neonatal seizures