Seizure Classification Research Articles

PO-04-033 CARDIONEUROABLATION IN A WOMAN WITH ICTAL-INDUCED CARDIAC ASYSTOLE

Ictal-induced cardiac bradyarrhythmia and asystole is a rare phenomenon. The exact mechanism of ictal-induced cardiac bradyarrhythmia and asystole remains unclear. It was postulated that stimulation of central autonomic network during ictal episode may trigger an abrupt burst of hypervagotonia. Prolonged episode of cardiac bradyarrhythmia and asystole may result in syncope or death due to impairment of cerebral perfusion. The role of cardioneuroablation (CNA) in this condition has not been well-described in the literature. To describe a case of successful CNA in a patient with ictal-induced bradyarrhythmia and asystole. n/a A 47-year-old female has a 1.5-year history of intractable focal epilepsy and COVID-19 infection. She started having multiple episodes of seizures following a mild COVID-19 infection. Electroencephalogram (EEG) and brain MRI revealed right temporal onset seizures without structural lesions. Due to ongoing uncontrolled seizures with multiple semiologies despite multiple anti-epileptic drugs, she was admitted to Epilepsy Monitoring Unit for seizure classification. Her ictal EEGs (Figure 1) showed onset of ictal rhythm in the right temporal region with episodes of severe sinus bradycardia (15-30 bpm) and sinus pauses (15-16 seconds). Telemetry tracings demonstrated PP interval slowing with PR interval prolongation prior to the pauses consistent with a vagally-mediated mechanism. Cardiac electrophysiology team recommended CNA for treating the episodes of ictal-induced bradyarrhythmia and asystole. 3D anatomic maps of the right atrium (RA) and left atrium (LA) were created using CARTO system (Biosense Webster). Right superior ganglionated plexus (RSGP) was localized with fractionation mapping and intracardiac echocardiography guidance. RSGP was targeted from the RA using an irrigated radiofrequency catheter with power limit of 25 W. Post-ablations of RSGP, her heart rate increased from 60 - 99 bpm. Additional lesions were delivered from the LA site but no additional heart rate increase was not seen. An implantable loop recorder was implanted post-ablation procedure. During follow-up of 8 months, she had recurrent focal epilepsy, but no bradyarrhythmias or asystole was noted on her loop recorder. Resting heart rates at long-term follow up were between 70 - 100 bpm. This case highlights the utility of CNA in patient with ictal-induced cardiac bradyarrhythmia and asystole. CNA may be an approach to avoid permanent pacemakers in this population.

Headache and epilepsy: prevalence and clinical variants

Headache, which is often present in patients with epilepsy, can occur outside of epileptic seizures as well as have a temporary connection with them – occur immediately before, during or after an attack. Clinical types of headache in epilepsy are considered in the International Classification of Headache, but not in the classification of epileptic seizures and types of epilepsy. Meanwhile, the presence of concomitant headache and its clinical phenotype should be taken into account when choosing treatment tactics in patients with epilepsy. The article presents data on the prevalence of different types of headache in patients with epilepsy and a modern classification of cephalalgia depending on the temporal relationship with an epileptic attack. Diagnostic criteria for four clinical variants of cephalgia are given: interictal, preictal, ictal and postictal. The comorbid relationship between epilepsy and migraine is considered in more detail, including the pathophysiological mechanisms underlying the combination of these paroxysmal conditions.

General and patient-specific seizure classification using deep neural networks

Seizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.

Epileptic Seizure Classification and Feature Optimization Technique Using Grey Wolf Algorithm on Dynamic Datasets

Epileptic Seizure Classification and Feature Optimization Technique Using Grey Wolf Algorithm on Dynamic Datasets

Epileptic seizure prediction and classification based on statistical features using LSTM fully connected neural network

Epilepsy is the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signals as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision.

Application of the International League Against Epilepsy Neonatal Seizure Framework to an international panel of medical personnel

The International League Against Epilepsy (ILAE) Neonatal Seizure Framework was tested by medical personnel. Attendees at the 2016 ILAE European Congress on Epileptology in Prague, the International Video-EEG Course in Pediatric Epilepsies in Madrid 2017, and a local meeting in Utrecht 2018, were introduced to the proposed ILAE neonatal classification system with teaching videos covering the seven types of clinical seizures in the proposed neonatal classification system. Five test digital video recordings of electroencephalography (EEG)-confirmed motor neonatal seizures were then shown and classified by the rater based on their knowledge of the proposed ILAE Neonatal Seizure Framework. A multi-rater Kappa statistic was used to assess the agreement between observers and the true diagnosis. The responses of 194 raters were obtained. There was no single predominant classification system that was currently used by the raters. Using the ILAE framework, 78%-93% of raters correctly identified the clinical seizure type for each neonate; the overall inter-rater agreement (Kappa statistic) was 0.67. The clonic motor seizure type was most frequently accurately identified (93% of the time; κ = 0.870). EEG technicians correctly identified all presented motor seizure types more frequently than any other group (accuracy = 0.9). The ILAE Neonatal Seizure Framework was judged by most raters to be better than other systems for the classification of clinical seizures. Among all seizure types presented, clonic seizures appeared to be the easiest to accurately identify. Average accuracy across the five seizure types was 84.5%. These data suggest that the ILAE neonatal seizure classification may be used by all healthcare professionals to correctly identify the predominant clinical seizure type.

Survival, Functional, and Seizure Control Outcomes After Resection of Perirolandic World Health Organization Grade II and III Gliomas: A Single-Center Retrospective Review.

We aimed to assess, in patients with perirolandic gliomas and gliomas originating from other regions, survival, functional outcomes, and seizure control and, in addition, to identify any clinical characteristics predictive of progression-free survival, overall survival, and seizure control. We retrospectively analyzed 87 patients who underwent resection of World Health Organization grade II or III gliomas at a single institution between 2009 and 2021. Tumors were classified by topographic involvement. One-year postoperative functional status was quantified with Karnofsky Performance Status. One-year seizure control was defined by Engel seizure classification. Dichotomous and categorical variables were reported as counts and percentages and compared using Fisher exact test. A Cox regression model was used to identify covariates that affect progression-free survival and overall survival. Patients with perirolandic gliomas had similar survival and functional outcomes to patients with gliomas from other regions and a low rate of lasting neurologic deficits. Patients with perirolandic gliomas had comparatively worse long-term seizure outcomes (approached statistical significance). Perirolandic involvement (hazard ratio [HR], 0.10; 95% confidence interval [CI], 0.02-0.46; P= 0.005) and preoperative seizures (HR, 0.14; 95% CI, 0.02-0.62; P= 0.017) conferred a lower likelihood of durable seizure control, whereas increased extent of resection (HR, 1.07; 95% CI, 1.03-1.12; P= 0.003) enhanced the likelihood of seizure freedom. Despite proximity to or presence in eloquent structures, perirolandic gliomas can largely be resected without incurring worse functional outcomes. Patients with perirolandic gliomas should be considered for maximal safe resection to optimize survival outcomes and improve seizure control.

Spatio-spectral feature classification combining 3D-convolutional neural networks with long short-term memory for motor movement/imagery

In this paper, we propose a novel EEG classification approach based on the Spatio-spectral feature, aiming to design a motor movement/imagery classification model that extracts multi-domain features with promising performance. Firstly, the difference between motor imagery (MI) tasks and real execution tasks was analyzed by calculating complexity measures of different frequency bands. Then, different connection patterns between MI tasks and real execution tasks were investigated by constructing the PLV and PLI networks. The results of the PLV and PLI brain networks showed that real execution tasks’ network connections intensity was stronger than MI tasks, which meant these two networks could be used to distinguish the EEG signal features of different tasks. Afterward, to fully explain the multi-domain features of EEG signals, we fused the Phase Locking Value (PLV) and Phase-Lag Index (PLI) matrices (spatial-domain) under the subsets of frequency bands (frequency-domain) into a 3-D feature, namely the Spatio-spectral feature. Finally, a 3-D convolutional neural network combined with a long short-term memory (3DCNN-LSTM) was utilized to decode the feature. The results showed that the average accuracy of 10 subjects, 20 subjects, 50 subjects, 80 subjects, and 103 subjects was 85.88%, 83.09%, 76.30%, 75.02%, and 74.54%. Taken together, the proposed method provided promising classification accuracies, superior multi-domain features extraction ability, simpler structure, and robustness to classify the different motor movement/imagery tasks. The results contribute to our understanding of applying the deep learning method to decode EEG multi-domain features in the brain-computer interface (BCI) systems (e.g., MI, emotion recognition, and epileptic seizure classification).

Investigation of changes in MDH enzyme activity in epilepsy

Aims: Epilepsy is one of the most common neurological diseases. It is classified into three groups: focal onset, generalized onset and unclassifiable. A seizure type is difficult to determine. The malate dehydrogenase (MDH) enzyme has a critical role in the excitability of the brain, and it has been reported that recurrent seizures are seen when its regulation is disturbed. We investigated the contribution of MDH enzyme levels as a biomarker to seizure classification. Methods: Our study was conducted prospectively by patients who were admitted to the emergency department of our hospital within a six-month period. Included in the study were 65 patients diagnosed with epilepsy, according to the International League Against Epilepsy (ILAE) classification. The patients were divided into two groups according to seizure type: focal onset and generalized onset. They were compared in terms of enzyme activity. Results: The MDH value was 47.56 ± 38.65 in patients with focal-onset epilepsy and 109.76 ± 128.44 in patients with generalized-onset epilepsy. Conclusion: Our results reveal the potential of MDH as a biomarker that can be used in epilepsy. In addition, the statistically significant difference between focal-onset and generalized-onset epilepsy indicates that it can be a usable biomarker in seizure classification

Study of electroencephalogram characteristics in neurological conditions in pediatric tertiary care hospital

Background: Electroencephalogram (EEG) remains a veritable tool in the diagnostic workup of seizures and childhood epileptic syndromes. EEG has a significant role in substantiating the clinical suspicion of epilepsy, the classification of seizures, and the management of childhood epilepsy. Methods: This study was retrospective descriptive with convenience sampling where two-years EEG recordings were included and the reporting were reviewed. The desired variables were encrypted and descriptive analysis made via statistical software SPSS version 20. Results: There was 499 EEG done in a two-year time period where the female children were (n=205, 41.1%) and mean age was 66 months. There were (n=317, 63.5%) children from out of Kathmandu valley. The most common indication for EEG recommendation was seizure without impaired consciousness (69.7%) and least common indication was seizure with impaired consciousness (8.4%). The rate of detection of EEG abnormalities in seizure without impaired consciousness was 43.9% while that in seizure with impaired consciousness was 38.1%. Among the most common EEG abnormality, abnormal sleep EEG record suggestive of generalized epilepsy most of the children had a history of seizure without impaired consciousness (71.4%) and least presented with seizure with impaired consciousness. Conclusions: The abnormality detection rate of epilepsy was higher in chance if done in early ages. The possibility of findings in EEG are higher with the history of seizures and the perinatal insult as well as birth asphyxia. EEG should be carried out to support the diagnosis of epilepsy in children, and a normal EEG won't be totally excluding epilepsy.

Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

The signals produced by an electrocardiogram (ECG) are made up of intricate pattern se-quences that have a periodic structure. These pattern sequences contain an initial P-wave, which denotes the beginning of an ECG wave, a QRS sequence, which denotes the intensi-ty of the pulse, and a T segment, which denotes the conclusion of the wave. Characteris-tics such as PR interval, QRS interval, QT interval, ST interval, R to R interval, etc. are employed to recognize chronic, ischemic, and other cardiac illnesses. These wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac disorders. Signal pre-processing, feature extraction, feature se-lection, classification into epileptic and non-epileptic seizures, and post-processing are some of these operations. For each of these operations, researchers create a wide range of algorithms. These algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection, filtering effectiveness, feature extraction & selection ef-fectiveness, and classifier effectiveness. Thus, researchers and system designers have be-come unclear when choosing the optimum algorithm set for an application. This text of-fers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen ambiguity. Convolutional neural network (CNN) based models outperform other models in terms of general-purpose per-formance, whereas application-specific deployments need the employment of customized fuzzy CNN models. A significant area of research & development is presented by the ob-servation that fuzzy logic techniques are not observed while constructing ECG classifica-tion models. Based on these findings, a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy values. With these parameters and a specially created CNN model, it was pos-sible to achieve an accuracy of 99.5% for diverse ECG datasets. This accuracy was com-pared to a number of cutting-edge models, and it was observed that the suggested model is quite good at categorizing ECG signals. The suggested technique was observed to be quicker than traditional procedures due to the usage of a fuzzy logic model, enhancing its scalability for a broad range of clinical applications.

Determinants of quality of life in Nigerian female patients with epilepsy on carbamazepine and levetiracetam monotherapy

BackgroundThe study is aimed to identify the predictors of Quality of Life (QOL) in women with epilepsy (WWE) on carbamazepine (CBM) and levetiracetam (LTM) monotherapy. 100 WWE were recruited (50 each on CBM and LTM), after clinical diagnosis of epilepsy supported by Electroencephalography (EEG) features and seizures classification by 2017 International League Against Epilepsy (ILAE) criteria, the Quality of Life Inventory Scale 31(QOLIE-31) and Zung Self-Rating Depression Scale (ZSRDS) were used to assess QOL and depression, respectively.ResultHigher QOLIE-31 scores was seen in the LTM group across all domains except seizure worry (p = 0.051) compared to CBM group. Logistic regression showed that the use of CBM (p = 0.000), fast frequency on EEG (p = 0.005), longer duration of epilepsy (p = 0.017), presence of depression (p = 0.008) and lower level of education (p = 0.003) were predictors of QOL. Progesterone (p = 0.040), oestradiol (p = 0.011) and prolactin (p = 0.002) in follicular phase showed significant association with QOLIE-total score. In the luteal phase, luteinizing hormone–follicle stimulating hormone (LH–FSH) ratio (p = 0.009) and testosterone (p = 0.015), FSH (p = 0.015), prolactin (p = 0.000), showed significant association with QOL. None of the hormones independently predicts QOL.ConclusionLTM group appears to have better QOL than CBM group. Healthcare providers should focus on addressing these identified predictors which include medication effect, depression, Level of education, EEG background and duration of epilepsy with aim of improving QOL.

Pre-processed and combined EEG data for epileptic seizure classification using deep learning

Epilepsy is a neurological disease that affects nearly 60 million people around the world. Electroencephalography (EEG) and Machine Learning (ML) can be used to detect epileptic seizures, where ML algorithms depend on hand crafted features. Recently, deep learning (DL) methods became popular due to automatic feature learning. However, current DL based approaches mainly focuses on different DL architecture design, especially deeper DL networks. Only few works experiment modified EEG data as an input to DL models. In this work, we propose modified, pre-processed and combined EEG signals as an input to different DL models for epilepsy seizure classification rather than using raw EEG signals. A variety of pre-processed and combined EEG signals have been evaluated with three different DL architectures: A simple Deep Neural Network (DNN) model and a moderately complex 1D-Convolutional Neural Network (CNN) model and a complex 1D-CNN model. We perform several experiments for two-class and three-class epileptic seizure classification on UCI-Bonn dataset using the proposed EEG signals: (1)Original EEG signal of UCI-Bonn dataset, (2)standardized EEG signal, (3)original EEG signal combined with squared EEG signal, and then standardized, (4)original EEG signal combined with differentiated EEG signal, and then standardized, and (5)original EEG signal combined with magnitude of Fast Fourier Transform (FFT) of EEG signal, and then standardized (original + FFT). Extensive evaluations and comparisons show that the best results are achieved with original + FFT combination on all DL architectures. Using original + FFT EEG signal, competitive results can be achieved with simple and moderately complex DL models, by utilizing less computational resources.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Focality in Febrile Seizures: A Retrospective Assessment Using Arterial Spin Labeling MRI.

Defining focality of febrile seizures (FS) in clinical practice remains controversial. We investigated focality issues in FS with a postictal arterial spin labeling (ASL) sequence. We retrospectively reviewed 77 children (median: 19.0 months, range: 15.0-33.0 months) who consecutively visited our emergency room for FS and underwent brain magnetic resonance imaging (MRI), including the ASL sequence, within 24 hours of seizure onset. ASL data were visually analyzed to assess perfusion changes. Factors related to the perfusion changes were investigated. The mean time to ASL acquisition was 7.0 (interquartile range: 4.0-11.0) hours. The most common seizure classification was unknown-onset seizures (n = 37, 48%), followed by focal-onset (n = 26, 34%) and generalized-onset seizures (n = 14, 18%). Perfusion changes were observed in 43 (57%) patients: most were hypoperfusion (n = 35, 83%). The temporal regions were the most common location of perfusion changes (n = 26, 60%); the majority of these were distributed in the unilateral hemisphere. Perfusion changes were independently associated with seizure classification (focal-onset seizures, adjusted odds ratio [aOR]: 9.6, p = 0.01; unknown-onset seizures aOR: 10.4, p < 0.01), and prolonged seizures (aOR: 3.1, p = 0.04), but not with other factors (age, sex, time to MRI acquisition, previous FS, repeated FS within 24 hour, family history of FS, structural abnormality on MRI, and developmental delay). The focality scale of seizure semiology positively correlated with perfusion changes (R = 0.334, p < 0.01). Focality in FS may be common, and its primary origin might be the temporal regions. ASL can be useful for assessing focality in FS, particularly when seizure onset is unknown.

Phase space reconstruction, geometric filtering based Fisher discriminant analysis and minimum distance to the Riemannian means algorithm for epileptic seizure classification

BackgroundMost clinicians observe the changes of the electroencephalograms (EEGs) via the visual inspection of the recorded signals of the patients. However, the visual inspection is time consuming. Also, it is subjective and empirical. Therefore, designing an accurate and an automatic epilepsy classification system has a profound significance for reducing the workload of the medical officers. MethodFirst, this paper applies the phase space reconstruction (PSR) method for constructing the covariance matrix for characterizing the chaotic states of the EEGs. The covariance matrices are symmetric positive definite (SPD) matrices used as the feature descriptors of the EEGs. This set of the covariance matrices is in fact a kind of the Riemannian manifold. Denote this Riemannian manifold as M. Secondly, the tangent space at the Riemannian geometric mean is computed via the logarithmic mapping operator. Denote this tangent space as TM. This tangent space TM is a kind of the Euclidean space containing a point in the Riemannian manifold M. That is, the SPD matrices in the Riemannian manifold M are mapped to the matrices in the tangent space TM at the Riemannian geometric mean via the logarithmic mapping operator. Then, these matrices in the tangent space TM are further mapped to the tangent feature vectors. The set of the tangent feature vectors is a kind of the vector space. Denote this vector space as VT. Thirdly, the transformation matrix based on the Fisher discrimination analysis (FDA) is used to map the high dimensional vectors in the vector space VT to the low dimensional vectors in another vector space. Then, the discriminative vectors are projected back to the Riemannian manifold M via the exponential mapping operator. Finally, the minimum distance to the Riemannian means (MDRM) algorithm is applied for performing the epileptic seizure classification. ResultsThe computer numerical simulations are conducted based on three well known EEGs epileptic seizure datasets. Overall, our proposed method achieves the best performances for three datasets compared to the state of the art methods. Also, the computational complexity of our proposed method is low. ConclusionOur proposed method is effective and robust for performing the seizure classification. Also, our proposed method can be implemented in the real time.

Epilepsia: generalidades sobre las crisis epilépticas y la epilepsia

Epilepsy is one of the most common neurological diseases in the world. It is calculated that 8% of the general population will have an epileptic seizure in their lifetime while 0.8%–1.7% will develop epilepsy. Therefore, given this disease's enormous importance in the daily clinical practice of both neurologists and general medicine physicians, in this update, we are going to focus on laying the foundations of basic knowledge to be able to manage it correctly. First, we will define basic concepts such as what a seizure is and what the disease of epilepsy is, broadly describing the pathogenic and pathophysiological process underlying an epileptic seizure. After reviewing these basic concepts, we will learn the classification of seizures and different types of epilepsy as well as etiologies that can cause them. To finish, we will broadly describe the epidemiology of this disorder that is so prevalent in our setting.

Perbedaan Manifestasi Klinis Kejang Demam Pada Anak Anemia dengan Tanpa Anemia di RSPAL dr. Ramelan Surabaya Tahun 2019-2022

Study aims: Febrile seizures are the most common neurological disorder in children. The incidence of febrile seizures is related to the incidence of epilepsy at 2-4% in the future and the probability of recurrence is about 30% and 50% after the first and second febrile seizures, respectively. Several recent studies have suggested that febrile seizures are more common in children with anemia. Anemia patients who experience febrile seizures can cause more severe clinical manifestations. Where 40.5% of the incidence of anemia in Indonesia occurs in children under the age of 5 years. Therefore, this study is conducted to determine the differences of the clinical manifestations of febrile seizures with and without anemia in children at RSPAL Dr. Ramelan Surabaya on 2019-2022.&#x0D; Methods: This study was an observational analytic study with a cross-sectional study approach and qualitative methods from secondary medical record data of infants and children aged 6 months-5 years at RSPAL Dr. Ramelan Surabaya for the period May 2019-May 2022. The number of samples in this study was 82 samples.&#x0D; Results: The characteristics of the sample which experiences the most febrile seizures occurres between the ages of 6-18 months, the gender of male with a temperature &gt;38°C and manifestations in the form of simple febrile seizures. In addition, the results of the fisher exact test with a significance level of 5% shows p = 0.96 which means there is no difference in the clinical manifestations of febrile seizures in anemic and non-anemic children.&#x0D; Conclusion: The conclusion of this study is that based on the classification of febrile seizures, the most common clinical manifestations that occur in children with anemia and without anemia are simple febrile seizures. So that there is no difference in the clinical manifestations of febrile seizures in anemic children and without anemia.

Additional overnight video EEG for the diagnosis of epilepsy: Experiences from Western Kenya

ObjectiveThe prolonged video EEG monitoring is widely used for the diagnosis and management of epilepsy, especially during the presurgical evaluation. The routine practice in neurology is to order a prolonged recording like an overnight EEG when the initial routine EEG is normal or unrevealing. Only few studies have evaluated this sequential approach and we aimed in this study to evaluate the added diagnostic value of a relatively brief video EEG monitoring especially in developing nations where the history of seizure semiology may be harder to obtain, and the video EEG monitoring technology is scarce. MethodsThis study analyzed retrospectively 167 overnight video EEG records in one of the secondary healthcare facilities in Western Kenya between March 2018 and March 2021. The indications were mainly further diagnosis and seizure classification. All the patients had an unrevealing routine EEG and 162 of them were normal. ResultsAdditional epileptiform discharges were recorded in 91 of those 162 with initial normal routine EEG. Further classification of seizure was achieved in 67 patients among 112 with initially unclassified seizure before the overnight recording. The improvement of 68% (97 out of 143 patients without a prior epilepsy diagnosis) for the diagnosis of epilepsy in those patients without initial final diagnosis is comparable to other similar studies but mostly with a longer duration of recording. The diagnosis was changed or at least improved in 142 (85%) patients out of the 167 patients who underwent the overnight video EEG. The treatment modification was immediately considered in 116 after the prolonged recording. ConclusionsAdding an overnight video EEG to an unrevealing routine EEG can significantly increase the likelihood of detecting additional epileptiform discharges in patients with epilepsy, thereby improving diagnostic yield and aiding in treatment adjustment for all patients suspected of having epilepsy. SignificanceThe sequential approach of adding a prolonged video EEG monitoring even as brief as an overnight video EEG to an unrevealing routine EEG has a very significant impact in further classification of seizure and diagnosis of epilepsy especially in a resource limited set up.

Chapter 15 - Motor seizure semiology

Motor semiology is a major component of epilepsy evaluation, which provides essential information on seizure classification and helps in seizure localization. The typical motor seizures include tonic, clonic, tonic-clonic, myoclonic, atonic, epileptic spasms, automatisms, and hyperkinetic seizures. Compared to the "positive" motor signs, negative motor phenomena, for example, atonic seizures and Todd's paralysis are also crucial in seizure analysis. Several motor signs, for example, version, unilateral dystonia, figure 4 sign, M2e sign, and asymmetric clonic ending, are commonly observed and have significant clinical value in seizure localization. The purpose of this chapter is to review the localization value and pathophysiology associated with the well-defined motor seizure semiology using updated knowledge from intracranial electroencephalographic recordings, particularly stereoelectroencephalography.