Seizure Status Research Articles

Promoting faster pathways to surgery: a clinical audit of patients with refractory epilepsy

BackgroundIndividuals with epilepsy who cannot be adequately controlled with anti-epileptic drugs, refractory epilepsy, may be suitable for surgical treatment following detailed assessment. This is a complex process and there are concerns over delays in referring refractory epilepsy patients for surgery and subsequent treatment. The aim of this study was to explore the different patient pathways, referral and surgical timeframes, and surgical and medical treatment options for refractory epilepsy patients referred to two Tertiary Epilepsy Clinics in New South Wales, Australia.MethodsClinical records were reviewed for 50 patients attending the two clinics, in two large teaching hospitals (25 in Clinic 1; 25 in Clinic 2. A purpose-designed audit tool collected detailed aspects of outpatient consultations and treatment. Patients with refractory epilepsy with their first appointment in 2014 were reviewed for up to six visits until the end of 2016. Data collection included: patient demographics, type of epilepsy, drug management, and assessment for surgery. Outcomes included: decisions regarding surgical and/or medical management, and seizure status following surgery. Patient-reported outcome measures to assess anxiety and depression were collected in Clinic 1 only.ResultsPatient mean age was 38.3 years (SD 13.4), the mean years since diagnosis was 17.3 years (SD 9.8), and 88.0% of patients had a main diagnosis of focal epilepsy. Patients were taking an average of 2.3 (SD 0.9) anti-epileptic drugs at the first clinic visit. A total of 17 (34.0%) patients were referred to the surgical team and 11 (22.0%) underwent a neuro-surgical procedure. The average waiting time between visit 1 to surgical referral was 38.8 weeks (SD 25.1), and between visit 1 and the first post-operative visit was 55.8 weeks (SD 25.0).ConclusionThe findings confirm international data showing significant waiting times between diagnosis of epilepsy and referral to specialist clinics for surgical assessment and highlight different approaches in each clinic in terms of visit numbers and recorded activities. A standardised pathway and data collection, including patient-reported outcome measures, would provide better evidence for whether promoting earlier referral and assessment for surgery improves the lives of this disease group.

Validating a model of benzodiazepine refractory nerve agent-induced status epilepticus by evaluating the anticonvulsant and neuroprotective effects of scopolamine, memantine, and phenobarbital

IntroductionOrganophosphorus nerve agents (OPNAs) irreversibly block acetylcholinesterase activity, resulting in accumulation of excess acetylcholine at neural synapses, which can lead to a state of prolonged seizures known as status epilepticus (SE). Benzodiazepines, the current standard of care for SE, become less effective as latency to treatment increases. In a mass civilian OPNA exposure, concurrent trauma and limited resources would likely cause a delay in first response time. To address this issue, we have developed a rat model to test novel anticonvulsant/ neuroprotectant adjuncts at delayed time points. MethodsFor model development, adult male rats with cortical electroencephalographic (EEG) electrodes were exposed to soman and administered saline along with atropine, 2-PAM, and midazolam 5, 20, or 40 min after SE onset. We validated our model using three drugs: scopolamine, memantine, and phenobarbital. Using the same procedure outlined above, rats were given atropine, 2-PAM, midazolam and test treatment 20 min after SE onset. ResultsUsing gamma power, delta power, and spike rate to quantify EEG activity, we found that scopolamine was effective, memantine was minimally effective, and phenobarbital had a delayed effect on terminating SE. Fluoro-Jade B staining was used to assess neuroprotection in five brain regions. Each treatment provided significant protection compared to saline + midazolam in at least two brain regions. DiscussionBecause our data agree with previously published studies on the efficacy of these compounds, we conclude that this model is a valid way to test novel anticonvulsants/ neuroprotectants for controlling benzodiazepine-resistant OPNA-induced SE and subsequent neuropathology.

A mouse model of seizures in anti-N-methyl-d-aspartate receptor encephalitis.

Seizures develop in 80% of patients with anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis, and these represent a major cause of morbidity and mortality. Anti-NMDAR antibodies have been linked to memory loss in encephalitis; however, their role in seizures has not been established. We determined whether anti-NMDAR antibodies from autoimmune encephalitis patients are pathogenic for seizures. We performed continuous intracerebroventricular infusion of cerebrospinal fluid (CSF) or purified immunoglobulin (IgG) from the CSF of patients with anti-NMDAR encephalitis or polyclonal rabbit anti-NMDAR IgG, in male C57BL/6 mice. Seizure status during a 2-week treatment was assessed with video-electroencephalography. We assessed memory, anxiety-related behavior, and motor function at the end of treatment and assessed the extent of neuronal damage and gliosis in the CA1 region of hippocampus. We also performed whole-cell patch recordings from the CA1 pyramidal neurons in hippocampal slices of mice with seizures. Prolonged exposure to rabbit anti-NMDAR IgG, patient CSF, or human IgG purified from the CSF of patients with encephalitis induced seizures in 33 of 36 mice. The median number of seizures recorded in 2weeks was 13, 39, and 35 per mouse in these groups, respectively. We observed only 18 brief nonconvulsive seizures in 11 of 29 control mice (median seizure count of 0) infused with vehicle (n=4), normal CSF obtained from patients with noninflammatory central nervous system (CNS) conditions (n=12), polyclonal rabbit IgG (n=7), albumin (n=3), and normal human IgG (n=3). We did not observe memory deficits, anxiety-related behavior, or motor impairment measured at 2weeks in animals treated with CSF from affected patients or rabbit IgG. Furthermore, there was no evidence of hippocampal cell loss or astrocyte proliferation in the same mice. Our findings indicate that autoantibodies can induce seizures in anti-NMDAR encephalitis and offer a model for testing novel therapies for refractory autoimmune seizures.

Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks

An increasing number of algorithms have been proposed for epileptic seizure prediction in recent years. But most of them are based on a partition of the electroencephalograph (EEG) signal of an epileptic patient into preictal, ictal (seizure), and interictal states. In this paper, we propose to further divide the preictal interval into multiple subintervals. Besides discriminating the seizure state from the preictal and interictal states, we also distinguish the preictal subintervals from each other. An epileptic state classification algorithm for epileptic EEG signals is then developed. The amplitude spectrums of EEG signals from 18 channels are firstly calculated and divided into 19 frequency subbands. The mean amplitude spectrum (MAS) on each of the 19 frequency subbands is then computed for each channel to form a MAS map of size 18 $$\times$$ 19. Finally, the MAS map is fed to a convolutional neural network (CNN) for feature extraction and a support vector machine (SVM) is employed for the epileptic state classification. Experiments show that, for a three-subinterval partition of the preictal state, a classification accuracy of 86.25% has been achieved on the CHB-MIT database by the MAS-based epileptic state classification algorithm using CNN and SVM.

Postoperative outcome of surgical interventions for epilepsy between 2005 and 2016 at the Epilepsy Center of Pécs

Epilepsy as a chronic, severe neurologic disease significantly influences the quality of life of the epileptic patients. In candidates well selected for surgery, the seizure freedom is realistically achievable, and the quality of life can be further improved with complex individual rehabilitation. We aimed to evaluate the postoperative outcome of patients who underwent epilepsy surgery between 2005 and 2016 at the Epilepsy Center at Pécs. We evaluated seizure status at regular follow-up visits after surgery and the quality of life using questionnaires focusing on employment and social status. 76% of the 72 patients who underwent surgical resection for epilepsy were free from disabling seizures , and 10% had rare disabling seizures (almost seizure-free), 7% experienced worthwhile improvement and 7% had no worthwhile improvement. Comparing the employment status of patients free from disabling seizures to patients not free from disabling seizures, we found that the employment status is significantly influenced by seizure freedom (p<0.01, Fisher's exact test). While 67% of seizure-free patients were employed, only 19% of patients not free from disabling seizures were hired. Our results resemble the international tendencies and success rate, proving epilepsy surgery as an available, valid and effective treatment in well selected patients. Orv Hetil. 2019; 160(7): 270-278.

New Approach for Automated Epileptic Disease Diagnosis Using an Integrated Self-Organization Map and Radial Basis Function Neural Network Algorithm

Currently, epilepsy disease (ED) is considered to be one of the gradual diseases in brain function over a period of several months or years. Seizure status is the primary common cause of ED. The main goal of this paper is to discover the seizure and epilepsy status using the prediction algorithm on the test results received from patient medical reports. This paper proposed an automatic epilepsy diagnostic method based on a self-organization map (SOM) method using a radial basis function (RBF) neural networks approach. The hybrid technique sought to enhance epilepsy diagnosis precision and to decrease the misdiagnosis of seizure disease. The SOM algorithm was employed to differentiate the unknown patterns of the seizure and epilepsy dataset. The experiments were performed on various RBF neural network algorithms with integrated SOM algorithms to predict and classify the standard epilepsy disease dataset. The hybrid method was tested on the UCI epilepsy dataset. The overall detection accuracy with 10-fold cross validation using SOM-RBF method achieved 97.47%. The results were compared with other modern classification techniques for seizure prediction and detection in terms of the evaluation factor.

Epileptic Seizure Detection With Permutation Fuzzy Entropy Using Robust Machine Learning Techniques

The automatic and accurate determination of the epileptogenic area can assist doctors in presurgical evaluation by providing higher security and quality of life. Visual inspection of electroencephalogram (EEG) signals is expensive, time-consuming and prone to errors. Several numbers of automated seizure detection frameworks were proposed to replace the traditional methods and to assist neurophysiologists in identifying epileptic seizures accurately. However, these systems lagged in achieving high performance due to the anti-noise ability of feature extraction techniques, while EEG signals are highly susceptible to noise during acquisition. The present study put forwards a new entropy index Permutation Fuzzy Entropy (PFEN), which may delineate between ictal and interictal state of epileptic seizure using different machine learning classifiers. 10-fold cross-validation has been used to avoid the over-fitting of the classification model to achieve unbiased, stable, and reliable performance. The proposed index correctly distinguishes ictal and interictal states with an average accuracy of 98.72%, sensitivity of 98.82% and a specificity of 98.63%, across 21 patients with six epileptic seizure origins. The proposed system manifests the fact that lower PFEN characterizes the EEG during seizure state than in the Interictal seizure state. The study also helps us to investigate the more profound enactment of different classifiers in term of their distance metrics, learning rate, distance, weights, multiple scales, etc. rather than the conventional methods in the literature. Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection. It also show’s its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.

Pharmacologically induced absence seizures versus kindling in Wistar rats.

OBJECTIVE:This study aimed to investigate the effects of γ-butyrolactone (GBL), a prodrug of gamma-Hydroxybutyric acid -induced absence seizures on the development of kindling in Wistar rats.METHODS:Three groups of adult male Wistar rats under anesthesia were implanted with bilateral cortical recording electrodes for the GBL group (GBL) and/or bipolar stimulation electrodes into the right basolateral amygdala for the Kindling group (KI) alone and Kindling plus GBL group (GBL+KI). Rats in the KI and GBL+KI groups were stimulated twice daily at the afterdischarge threshold until they reached Racine’s stage 5 seizure state. The animals in the GBL + group had an i.p injection of GBL 20 minutes before each electrical stimulation, and the effects of GBL-induced seizures on the development of kindling were investigated. The animals in the GBL group were injected GBL twice daily i.p. for 15 days without receiving any electrical stimulation.RESULTS:The KI animals reached stage 5 seizure stage at 12th stimulations, whereas the GBL+KI rats reached at 27th stimulations. The mean numbers of stimulations needed for the development of the first stage 3, 4, or 5 generalized seizures were significantly higher in the GBL+KI group than the KI group.CONCLUSION:The resistance to amygdala kindling in the GBL model can be modulated by the absence seizure mechanism alone, without the intervention of an abnormal genetic background.

Network Properties Revealed during Multi-Scale Calcium Imaging of Seizure Activity in Zebrafish.

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small “windows” in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy.

Classification of Scalp EEG States Prior to Clinical Seizure Onset

Objective: To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group. Results: Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. Discussion: The MSC could be a useful approach for seizure-monitoring both in the clinic and at home. Methods: Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.

EEG-based seizure detection in patients with intellectual disability: Which EEG and clinical factors are important?

Abstract Epilepsy is a commonly secondary disability in people with an intellectual disability (ID), affecting 22% of the ID population while 1% of general population. Surprisingly, EEG-based automated seizure detection in the ID population has not yet been sufficiently studied. The reasons are twofold. Firstly, long-term EEG recordings are few due to behavioral problems. Secondly, the annotation of EEG recordings has been proved difficult due to the complex EEG signal abnormalities caused by brain development disorders. As a result, the performance of automated seizure detection for ID people is largely unknown. In this work, we performed automated seizure detection on a retrospective dataset containing 615 h ambulatory scalp EEG from 29 participants with ID, including 91 seizures. To design a generic seizure detector for the ID people, we need to deal with three major problems: highly imbalanced data, heterogeneous dataset and difficult annotation. (1) For the imbalanced data, we used proper performance criteria (e.g., precision and recall curve) and employed a post-processing process (i.e., patient-specific detection thresholds). (2) For the heterogeneous dataset, we employed multi-domain EEG features that showed a better discriminative power in our dataset, and compared the linear and nonlinear (LDA vs. SVM with Gaussian kernel) classifiers and validated using a leave-one-out cross validation (LOOCV). (3) A stepwise EEG annotation procedure was used to improve the accuracy of annotation due to the presence of numerous seizure imitators and unclear contrast between ictal and interictal EEG activities. Results showed that LDA outperformed SVM with a clear margin of sensitivity, and achieved overall sensitivities 63.1–81.3%, a median FD/h of 1.0 and median latency of 11.5 s. Finally, we conclude that EEG signals of the ID population form a heterogeneous entity with respect to important factors: EEG discharge patterns, EEG backgrounds and EEG seizure visibility. The performance of the seizure detection varies significantly with these factors. The results presented here can serve as prior knowledge for designing a generic seizure detector for the ID patients and the non-convulsive seizure states (NCSS).

Calcium imaging, MEA recordings, and immunostaining images dataset of neuron-astrocyte networks in culture under the effect of norepinephrine.

BackgroundMonitoring the activity and morphology of neuron-astrocyte networks in culture is a powerful tool for studying dynamics, structure, and communication in neuron-astrocyte networks independently or as a model of the sub-brain network. These cultures are known to produce stereotypical patterns of activity, e.g., highly synchronized network bursts resembling sleep or seizure states, thus it enables the exploration of behaviors that can relate to brain function and disease. High-resolution microscopy of calcium imaging combined with simultaneous electrical recording provides a comprehensive overview on the network's dynamics. This setup makes it possible to apply global perturbations of electrical and chemical stimulation on the cultures during the recording task and to record the effects on network activity on-line. Morphological changes in the cultures can be obtained to have a complete dataset for structure-function study of neuron-astrocyte networks in vitro.FindingsThe 4 TB of data presented here was recorded and imaged as part of an accompanying study looking at in vitro structure-function of neuron-astrocyte networks. Simultaneous optical (calcium imaging) and electrical (micro-electrode array) recordings lasted 5–12 minutes and included spontaneous activity recording, electrical and chemical stimulation of neuron-astrocyte, and isolated astrocyte cultures. The data include activity recordings of 58 different cultures, with 1–2 regions of interest recorded for each culture. Production procedures, experimental protocols, and reuse options are included. The data have been suitable to reveal changes in the activity and morphology of the cultures and enabled observation and analysis of neuron-astrocyte and isolated astrocyte culture behaviors under the applied perturbations.ConclusionsOur dataset is sufficient to show significant changes in activity and morphology of neuron-astrocyte networks in culture under the applied stimulations. More than 100 recordings of 58 different cultures give insight of the observation's significance and led to conclusions about astrocyte activity and neuron-astrocyte network communication. Making it available here will allow others to test new tools for calcium imaging analysis and extracellular neuronal voltage recordings.

Current knowledge on Hashimoto's encephalopathy: a literature review.

Hashimoto's encephalopathy is a rare disease, with a reported prevalence of 2.1 per 100 000. Clinical manifestations include confusion, decreased state of consciousness, cognitive deficit, seizures, myoclonus, ataxia, and focal neurological deficits. Due to the wide variety of signs and symptoms, clinical diagnostic suspicion is essential. Diagnosis is based on three pillars: the presence of neurological clinical manifestations after ruling out other causes of encephalopathy. 2) Presence of increased antithyroid antibodies. 3) Significant clinical improvement after the administration of immunomodulation. The treatment of Hashimoto's encephalopathy pursues two objectives: to control the autoimmune process and to control the complications of the disease. Although in most cases recovery is complete with treatment, the risk of relapse can range from 12.5 to 40% in follow-ups to 2 years.

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network.

A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. This calculation, which considers the differences between the sequence pairs to build a reasonable partition, can improve the classification performance. On this basis, however, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under circumstances of insufficient a priori knowledge of domain dependency, such as denoising, feature extraction on a special patient, etc. To address these problems by (1) effectively calculating the MI matrix (synchronization pattern) and (2) accurately classifying the seizure or non-seizure state, this study first accurately measures the synchronization between channel pairs in terms of affinity propagation clustering partition MI (APCPMI). The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs into a correlation matrix. Finally, a cross-layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify seizure or non-seizure states automatically. Experiments are performed using the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states are identified with an accuracy, sensitivity, and specificity of 0.9793 ± 0.002, 0.9942 ± 0.0005, and 0.9676 ± 0.003, respectively; the resulting performance is superior to those achieved by most existing methods over the same dataset. Furthermore, the approach alleviates the necessity for strictly preprocessing (denoising, removing interferences and artifacts) the EEG data using prior knowledge, which is usually required by existing approaches.

Seizure predictors and outcome after Onyx embolization in patients with brain arteriovenous malformations.

Brain arteriovenous malformation (bAVM)-related epilepsy can significantly affect patient quality of life. We aimed to identify the factors associated with seizures occurrence and evaluate the long-term outcome following Onyx embolization in bAVM patients. Between July 2014 and July 2016, 239 consecutive patients underwent treatment for bAVMs in our institute and were respectively analyzed. Demographics, seizure status and bAVM morphologic characteristics were recorded. Modified Engel classification was used to evaluate the long-term seizure outcomes. Of 239 bAVM patients, 68 (28.5%) initially presented with seizures. Seizure occurrence was associated with cerebral hemorrhage history, frontal-temporal location and arterial borderzone location. Of the 37 patients who presented with initial seizures and were treated with Onyx embolization, 23 (62.2%) were treated with antiepileptic drugs (AEDs) before Onyx embolization. At the last follow-up visit, 19 (51.4%) of the 37 patients reached modified Engel class I outcome. Of the 23 patients who had ever been treated with AEDs, 12 (52.2%) were still taking AEDs at the last follow-up visit. Single-factor analysis showed that arterial borderzone location was significantly correlated with higher modified Engel class outcome ( p = 0.046). Patients with bAVM hemorrhage history, frontal-temporal location and arterial borderzone location were associated with seizure occurrence. Seizure-free status was not obtained in AVM patients with arterial borderzone after embolization, though it may have benefits in other ways. The seizure-free mechanism of bAVM with Onyx embolization is worth further study.

Уровень кортикостерона в крови у крыс с разным уровнем предрасположенности к аудиогенной эпилепсии

Актуальность. Селектированная на проявление судорог в ответ на сильный звук (аудиогенную эпилепсию, АЭ) линия крыс Крушинского-Молодкиной (КМ) - модель судорожных состояний человека, c быстрым развитием судорожного припадка в ответ на включение звука (100-120 дБ). Однако у крыс с АЭ изменений уровня кортикостерона (КС) в крови в связи с судорогами не определяли, хотя анализ связи АЭ и стресс-реакции - важная практическая задача. Методы. Уровень КС в образцах сыворотки крови определяли с помощью набора у крыс линии КМ, а также у крыс линии «0», селектированных из гибридной популяции КМ х Вистар на отсутствие АЭ, и у крыс популяции Вистар. Результаты. У всех крыс КМ был зарегистрирован аудиогенный припадок, тогда как у крыс линии «0» и Вистар судорог не было. Через 30 мин после действия звука (и судорог) у крыс КМ повышеается уровень КС, тогда как сразу после судорог (через 1-3 мин) данная реакция не обнаруживается. У крыс линии «0» обнаружен достоверно более высокий фоновый уровень КС, по сравнению с КМ и Вистар. Уровень КС у них, как и у крыс Вистар, после действия звука не изменялся. Заключение. Подъем уровня КС в крови после действия звука наблюдали только у крыс КМ через 30 мин после припадка АЭ. Крысы линии «0», у которых нет судорог в ответ на звук, обнаружили более высокий, чем у КМ и Вистар уровень КС в фоне. Aims. The Krushinsky-Molodkina (KM) rat strain, which was selected for a seizure response to a sound (audiogenic epilepsy, AE), is known as a model of human seizure states. Although the association of blood CS with stress reaction is an important practical issue, blood levels of corticosterone (CS) have never been analyzed in rats with AE in relation with seizures. Methods. Serum concentration of CS was measured using IBL International Corticosterone ELISA kits in KM rats, rats of the «0» strain, which was selected (based on F2 KM х Wistar hybrids) for the lack of seizures in response to a sound, and Wistar rats. Results. All KM rats developed AE seizures in response to a sound while no seizures were observed in rats of the «0» strain and Wistar rats. The increase in blood CS was observed only in KM rats at 30 min after AE seizure. At background, the level of CS was significantly higher in rats of the «0» strain, which did not develop seizures in response to a sound, than in KM and Wistar rats. CS levels remained unchanged in both «0» and Wistar rats after the sound exposure. Conclusions. The increase in blood CS occurred in KM rats at 30 min after the seizure episode. At background, the CS level was higher in rats of the «0» strain, which did not respond with seizures to a sound, than in Wistar and KM rats. Therefore, the blood level of CS depends in a complicated way on both AE and the selection history of the «0» rat strain.

Presumed Neuroglycopenia Caused by Severe Hypoglycemia in Horses.

BackgroundNeuroglycopenia refers to a shortage of glucose in the brain resulting in neuronal dysfunction and death if left untreated. Presumed neuroglycopenia has not been described in horses.ObjectiveTo report neurological signs in horses with presumed neuroglycopenia as the result of severe hypoglycemia.AnimalsNinety horses (hours to 28 years of age) diagnosed with hypoglycemia (blood glucose concentration < 75 mg/dL [< 4.2 mmol/L]).MethodsRetrospective study. Electronic medical records were searched. Signalment, history, complaint, clinical signs, laboratory findings including CSF analysis, electroencephalogram, clinical or definitive diagnosis, and outcome were recorded. Kruskal‐Wallis analysis of variance and logistic regression were used to investigate association between blood glucose concentration and data extracted. Statistical significance was set at P < 0.05.ResultsThirty‐eight and 52 horses had mild (50‐74 mg/dL [2.8‐4.1 mmol/L]), and severe hypoglycemia (< 50 mg/dL [< 2.8 mmol/L]), respectively. Most common causes of hypoglycemia included liver and gastrointestinal (40%) disease, sepsis (33%), neoplasia (7%), and insulin‐induced (4%). Most common neurologic deficits included obtundation (100%), seizures (42%), and disorientation (22%). CSF‐glucose was severely low (mean 2.5 mg/dL [0.1 mmol/L], median 0 mg/dL). Paroxysmal discharges in support of seizures were identified in the occipital (visual) and parietal (closest to temporal‐auditory) cortical regions upon EEG examination (8/8 horses).Conclusions and clinical importanceNeuroglycopenia is presumed to occur in horses as the result of severe hypoglycemia. Subclinical seizures, and intermittent blindness and deafness of cortical origin can occur. Severe altered state of consciousness and seizures can be observed at a blood glucose cut‐off value of < 42 mg/dL (< 2.3 mmol/L).

Spiking patterns of a neuron model to stimulus: Rich dynamics and oxygen's role.

Neuronal spiking patterns, which are of fundamental importance for the understanding of information processing in neural systems, can be generated in response to different stimuli. We here investigate in detail the stimulus-induced spiking patterns in a biologically plausible neuron model in which the oxygen concentration and the dynamical concentrations of potassium, sodium, and chloride are considered. Various types of spiking patterns can be induced by the different external potassium accumulations in response to the stimulus, including two different types of epileptic seizure (SZ) and spreading depression (SD) states, two different mixed states of SD and SZ, SZ state with multi-burst, and tonic firing behaviors. Interestingly, we show that these rich spiking patterns can also be induced by the current stimulus with a low oxygen concentration. Furthermore, we reveal that the stimulus can induce two different phase transitions from the SD state to the SZ state according to the phase transition theory, which results in the different electrical activities. All these findings may provide insight into information processing in neural systems.

Clinical characteristics of acute encephalopathy with acute brain swelling: A peculiar type of acute encephalopathy

ObjectivesAcute encephalopathy has been observed with acute brain swelling (ABS) that is characterized by rapid progression to whole-brain swelling. The objective of this study was to describe the clinical characteristics of ABS. MethodsWe encountered four patients with ABS and retrospectively investigated their clinical data with a medical chart review. ResultsThree patients had seizure clustering or status epilepticus in the clinical course. Signs of elevated intracranial pressure (ICP) appeared 3–9 h after the first convulsive attack in three patients. In all patients, signs of brainstem involvement appeared 1–8 h after signs of elevated ICP. Mild hyponatremia that progressed after signs of elevated ICP appeared was noted in three patients. Brain CT revealed mild brain swelling in the initial phase, which rapidly progressed to whole-brain swelling. No focal abnormalities were detected on brain MRI in one patient. Continuous electroencephalography was initially normal, but in two patients, high-amplitude slow waves appeared with rapid changes before signs of brainstem involvement. Although recovery was achieved without sequelae in two patients, outcome was fatal for the other two. ConclusionsThe pathogenesis of ABS has yet to be clarified, but clinical features in our patients are not consistent with any established subtypes of acute encephalopathy. Therefore, we believe that ABS should be recognized as a new type of acute encephalopathy.

An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states

Electroencephalography (EEG) is a measurement tool to measure the electrical activity of brain observed due to chemical variation in brain. The EEG analysis has important role in feature extraction and classification methods for detecting and predicting various brain diseases. Epilepsy is a major disease characterized by seizures and observed due to sudden abnormal electrical discharges in the brain. Mostly the research is carried out to classify the normal EEG signal from epileptic EEG signal. The objective of the paper is to identify pre-seizure state and seizure state of EEG signal using time and frequency features. Classifying these states of EEG signal using fuzzy classifier helps in predicting seizures. The methodology uses hypothetical testing to refine feature selection and pattern adapted wavelet transform to improve classification. The appropriate feature selection reduces computational complexity of the classifier. The artifacts are removed from the EEG signal using Independent Component Analysis (ICA). The CHB-MIT EEG scalp dataset from Children’s Hospital, Boston is used for experimentation. The result shows classification accuracy of 96.48 %, True Positive Rate 96.52% and False Positive Rate 0.352 for seizure detection and 96.02% accuracy for Pre-seizure state detection with 13–110 s earlier than the onset seizures.