Electroencephalography In Epilepsy Research Articles

Analysis of neural fragility in epileptic zone based on stereoelectroencephalography

There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.

Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used to study the electrical activity of the human brain. The machine learning-based classifier is utilized to detect the seizure by manually extracting the features from the EEG signals in previous works. Though, these effective benefits have been proved already with automatic feature extraction, they are unable to achieve the classification of multiple classes. Meanwhile, real-time epileptic seizure detection is unable to keep the capacity because the identifiable EGG is too long. Hence, this paper is decided to develop an enhanced deep learning architecture with EEG signal for performing automatic epileptic seizure detection. The EEG signal is collected from the standard datasets. The Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform (FBSE-EWT) method is used to decompose the signal from the gathered data. In feature extraction, the decomposed signals are used and while extracting the signal features, the autoencoder is involved in that process. To reduce the computational overload, the relief-F feature ranking method is used for choosing the important signal features. The Long Short Term Memory (LSTM) and Multi-Scale Atrous-based Deep Convolutional Neural Networks (MSA-DCNN) are used that is named as Hybrid Deep Scheme (HDS) to detect epileptic seizures with the top-ranked features for epileptic seizure classification. The Black Widow Optimization (BWO) and the Spider Monkey Optimization (SMO) are combined to generate Adaptive Spider Monkey Black Widow Optimization (ASMBWO) that is employed to perform the parameter tuning into a classification technique. Using different measures, the experimental analysis is done between the conventional epileptic seizure detection and the proposed model to establish an enhanced performance of the proposed model.

A Moth–Flame Optimized Echo State Network and Triplet Feature Extractor for Epilepsy Electro-Encephalography Signals

The analysis of epilepsy electro-encephalography (EEG) signals is of great significance for the diagnosis of epilepsy, which is one of the common neurological diseases of all age groups. With the developments of machine learning, many data-driven models have achieved great performance in EEG signals classification. However, it is difficult to select appropriate hyperparameters for the models to file a specific task. In this paper, an evolutionary algorithm enhanced model is proposed, which optimizes the fixed weights of the reservoir layer of the echo state network (ESN) according to the specific task. As evaluating a feature extractor relies heavily on the classifiers, a new feature distribution evaluation function (FDEF) using the label information of EEG signals is defined as the fitness function, which is an objective way to evaluate the performance of a feature extractor that not only focuses on the degree of dispersion, but also considers the relation amongst triplets. The performance of the proposed method is verified on the Bonn University dataset with an accuracy of 98.16% and on the CHB-MIT dataset with the highest sensitivity of 96.14%. The proposed method outperforms the previous EEG methods, as it can automatically optimize the hyperparameters of ESN to adjust the structure and initial parameters for a specific classification task. Furthermore, the optimization direction by using FDEF as the fitness of MFO no longer relies on the performance of the classifier but on the relative separability amongst classes.

Topological inference and correlation of signals with application to electroencephalography in epilepsy

Electroencephalography (EEG) is an important neurophysiological modality for understanding brain functions and disorders. Topological signal processing allows us to capture how local variations in EEG signals affect their global structures. It reveals changes of topological structures across different temporal and spectral scales of signals that may not be observable through the standard signal processing methods. A topological signal processing framework that tracks the evolution of time segments corresponding to electric potential below a horizontal threshold has shown promise in applications to EEG studies of brain disorders. Gradient filtration is a generalization of the framework for extracting topological features in a signal treated as a two-dimensional curve and tracks the evolution of arcs in the curve cut off by a straight line moving in an arbitrary direction. In this study, we set up a statistical inference framework on gradient filtrations and an improved topological correlation measure for EEG signals by correlating features across gradient filtrations in multiple directions. We compare its performance with standard correlation measures in simulation studies and application to intracranial EEG signals recorded in canine with epilepsy.

Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network.

In order to realize the early prediction of refractory epilepsy in children, data preprocessing technology was used to improve the data quality, and the detection model of refractory epilepsy in children based on convolutional neural network (CNN) was established. Then, the data in the epilepsy electroencephalography (EEG) signal public data set was used for model training and the diagnosis of refractory epilepsy in children. Moreover, back propagation neural network (BPNN), support vector machine (SVM), XGBoost, gradient boosting decision tree (GBDT), AdaBoost algorithm were introduced for comparison. The results showed that the early prediction accuracy of BP, SVM, XGBoost, GBDT, AdaBoost, and the algorithm in this study for refractory epilepsy in children were 0.745, 0.778, 0.885, 0.846, 0.874, and 0.941, respectively. The sensitivities were 0.81, 0.826, 0.822, 0.84, 0.859, and 0.918, respectively. The specificities were 0.683, 0.696, 0.743, 0.792, 0.84, and 0.905, respectively. The accuracy was 0.707, 0.732, 0.765, 0.802, 0.839, and 0.881, respectively. The recall rates were 0.69, 0.716, 0.753, 0.784, 0.813, and 0.877, respectively. F1 scores were 0.698, 0.724, 0.759, 0.793, 0.826, and 0.879, respectively. Through the comparisons of the above six indicators, the algorithm proposed in this study was significantly higher than other algorithms, suggesting that the proposed algorithm was more accurate in early prediction of refractory epilepsy in children. Analysis of the EEG characteristics and magnetic resonance imaging (MRI) images of refractory epilepsy in children suggested that the MRI images of patients' brains under this algorithm had obvious characteristics. The reason for the prediction error of the algorithm was that the duration of epilepsy was too short or the EEG of the patient didn't change notably during the epileptic seizure. In summary, the prediction method of refractory epilepsy in children based on CNN was accurate, which had broad adoption prospects in assisting clinicians in the examination and diagnosis of refractory epilepsy in children.

Evaluation of Tuberous Sclerosis Complex Patients

Aim: Tuberous sclerosis complex (TSC) is a multisystem genetic, autosomal-dominant disorder predisposing to multiple organ manifestations. The aim of this study is to determine TSC the frequency of findings including diagnostic and non-diagnostic criteria.Patients and Metod: Thirty-five patients diagnosed with tuberous sclerosis complex were examined retrospectively. The diagnosis of the patients were evaluated according to the diagnostic criteria of TSC that were updated in 2012. As non-diagnostic criteria, we reviewed epilepsy, drug-resistant epilepsy, electroencephalography (EEG) types (focal, diffuse-multifocal and hypsarrhythmia) and TAND (TSC-associated neuropsychiatric disorders) (intellectual disability and/or autism and learning disability).Results: Twenty-one cases (60%) presented with seizures, 9 cases (26%) with hypopigmented patches and 5 cases (14%) with cardiac rhabdomyomas. The most common finding with brain magnetic resonance imaging (MRI) was cortical tubers (85%). EEG examinations revealed diffuse and multifocal epileptic disorder in 5 (24%), focal epileptic disorder in 8 (38%), and hypsarrhythmia in 8 (38%) patients. 38% of the patients with epilepsy were diagnosed with refractory epilepsy. Severe intellectual disability and / or autism were detected in 11 (32%) patients. The number of patients with renal angiomyolipoma (p:0.001) were significantly higher in drug resistant epilepsy patients and also TSC-associated neuropsychiatric disorders (TAND) (p:0.001) rate was significantly higher in epilepsy patients.Conclusion: The disease should be followed with a multidisciplinary approach. Although not included in the diagnostic criteria, it should be kept in mind that epilepsy, intellectual disability and neuropsychiatric disorders frequently accompany.

Electroencephalography in epilepsy: look for what could be beyond the visual inspection.

Since its starting point in 1929, human scalp electroencephalography (EEG) has been routinely interpreted by visual inspection of waveforms using the assumption that the activity at a given electrode is a representation of the activity of the cerebral cortex under it, but such a method has some limitations. In this review, we will discuss three advanced methods to obtain valuable information from scalp EEG in epilepsy using innovative technologies. Authors who had previous publications in the field provided a narrative review. Spike voltage topography of interictal spikes is a potential way to improve non-invasive EEG localization in focal epilepsies. Electrical source imaging is also a complementary technique in localization of the epileptogenic zone in patients who are candidates for epilepsy surgery. Quantitative EEG simplifies the large amount of information in continuous EEG by providing a static graphical display. Scalp electroencephalography has the potential to offer more spatial and temporal information than the traditional way of visual inspection alone in patients with epilepsy. Fortunately, with the help of modern digital EEG equipment and computer-assisted analysis, this information is more accessible.

Nonictal EEG biomarkers for diagnosis and treatment.

SummaryThere are no reliable nonictal biomarkers for epilepsy, electroencephalography (EEG) or otherwise, but efforts to identify biomarkers that would predict the development of epilepsy after a potential epileptogenic insult, diagnose the existence of epilepsy, or assess the effects of antiseizure or antiepileptogenic interventions are relying heavily on electrophysiology. The most promising EEG biomarkers to date are pathologic high‐frequency oscillations (pHFOs), brief EEG events in the range of 100 to 600 Hz, which are believed to reflect summated action potentials from synchronously bursting neurons. Studies of patients with epilepsy, and experimental animal models, have been based primarily on direct brain recording, which makes pHFOs potentially useful for localizing the epileptogenic zone for surgical resection, but application for other diagnostic and therapeutic purposes is limited. Consequently, recent efforts have involved identification of HFOs recorded with scalp electrodes, and with magnetoencephalography, which may reflect the same pathophysiologic mechanisms as pHFOs recorded directly from the brain. The search is also on for other EEG changes that might serve as epilepsy biomarkers, and candidates include arcuate rhythms, which may reflect repetitive pHFOs, reduction in theta rhythm, which correlates with epileptogenesis in several rodent models of epilepsy, and shortened sleep spindles that correlate with ictogenesis.

Proposed consensus definitions for new-onset refractory status epilepticus (NORSE), febrile infection-related epilepsy syndrome (FIRES), and related conditions.

We convened an international group of experts to standardize definitions of New-Onset Refractory Status Epilepticus (NORSE), Febrile Infection-Related Epilepsy Syndrome (FIRES), and related conditions. This was done to enable improved communication for investigators, physicians, families, patients, and other caregivers. Consensus definitions were achieved via email messages, phone calls, an in-person consensus conference, and collaborative manuscript preparation. Panel members were from 8 countries and included adult and pediatric experts in epilepsy, electroencephalography (EEG), and neurocritical care. The proposed consensus definitions are as follows: NORSE is a clinical presentation, not a specific diagnosis, in a patient without active epilepsy or other preexisting relevant neurological disorder, with new onset of refractory status epilepticus without a clear acute or active structural, toxic or metabolic cause. FIRES is a subcategory of NORSE, applicable for all ages, that requires a prior febrile infection starting between 2 weeks and 24 hours prior to onset of refractory status epilepticus, with or without fever at onset of status epilepticus. Proposed consensus definitions are also provided for Infantile Hemiconvulsion-Hemiplegia and Epilepsy syndrome (IHHE) and for prolonged, refractory and super-refractory status epilepticus. This document has been endorsed by the Critical Care EEG Monitoring Research Consortium. We hope these consensus definitions will promote improved communication, permit multicenter research, and ultimately improve understanding and treatment of these conditions.

Recognition of epilepsy electroencephalography based on AdaBoost algorithm

Automatic recognition of epilepsy electroencephalography (EEG) signal has become a research focus because of its high efficiency, and many algorithms have been put forward to achieve it. As one of the classic algorithms of boosting algorithm, AdaBoost algorithm has been widely used in face detection and target tracking fields, but the algorithm also has a disadvantage that is its degradation. In order to solve this problem, this paper puts forward three measures to optimize the algorithm by filtering the weak classifiers whose recognition rates are low, introducing the smoothing factor and a weighted correction function. In order to verify the robustness of optimized algorithm, we choose three main parameters, i.e., the number of weak classifier, which is denoted by T; the base of logarithmic function, which is denoted by α; the threshold of weight, which is denoted by β. The experimental results of optimized AdaBoost show that it has good robustness and high recognition rate. #br#In this paper, we divide the whole process into three steps. The first step is to use the Butterworth digital low-pass filter in which the cutoff frequency of pass band is 40 Hz to filter noise whose frequency is above 40 Hz. The second step is to do feature extraction with the help of wavelet packet decomposition. The third step is to compute the sum of absolute value which are the wavelet packet coefficients of fourth layer, the wavelet package entropy and the sum of signal amplitude square and combine them together to form the feature vector of each EEG. Because the wavelet package entropy is far less than the sum of absolute value and the sum of signal amplitude square, in order to make sure that the entropy reacts in the third step, we use one thousandth of the sum of absolute wavelet packet coefficients, one hundredth of the sum of signal amplitude square and the wavelet package entropy as the weighted feature vector. Finally, we succeed in distinguishing EEGs between epilepsy and normal by using the optimized AdaBoost whose input is the weighted feature vector. The result shows that the presented method has a high recognition rate, it can identify 96.11% epilepsy EEGs and 99.51% normal EEGs, thus it provides an effective solution for the correct diagnosis of epilepsy.

Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with −1 and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.

The prognostic value of electroencephalography in epilepsy: a long-term follow-up study

Predicting the evolution of epilepsy is of obvious importance for patients and their families. Value of electroencephalography (EEG) is extensively used in the diagnosis of epilepsy yet its role as a prognostication method remains unclear. The aim of the present retrospective study is to investigate the relationship between serial EEG recordings and long-term clinical and social outcomes in a cohort of patients with epilepsy. Thirty-nine epileptic patients were monitored clinically and with repeat EEG recordings for more than 15 years. All patients who initially had epileptiform discharges ended up with poor or moderate seizure control whereas more than half of the patients with normal initial recordings had good clinical outcomes and satisfactory social adjustment. Deterioration of the recordings over time was associated with unfavourable results in a significant proportion of patients (90%), while stable or improved EEG findings predicted a favourable outcome. It is concluded that serial EEG recordings can be used in the prognostic evaluation of epilepsy.

Hyperventilation activation on EEG recording in children with epilepsy

In 20 patients with epilepsy, electroencephalography (EEG) slowing was quantitatively characterized during standardized hyperventilation activation (respiratory rate: 30/min, threefold elevation of total expiratory volume, duration: 4 min) and changes in cerebral blood flow and velocity in the right common carotid artery were monitored with the Doppler ultrasonic method. Thirteen age-matched normal children served as controls. The results were as follows: (1) EEG slowing in the epilepsy group was greater compared with controls. (2) There was a significant decrease in mean frequency (decrease in ci power and increase in δ power) during hyperventilation in the epilepsy group, but no significant change in the controls. (3) The decrease in cerebral blood flow (CBF) was greater in the epilepsy group at the beginning of hyperventilation, possibly related to the greater EEG slowing. (4) The percentage of CBF at the end of hyperventilation was similar in the epilepsy and control groups. The difference in EEG response to hyperventilation between the 2 groups may be due to differences in the decrease in CBF volume and the sensitivity of the change in CBF.

William G. Lennox: a remembrance.

William G. Lennox, author of Epilepsy and Related Disorders, had a lasting effect on our understanding of this illness. He postulated that epilepsy was not a unitary condition and that neuronal chemistries differed from one form of the disease to another. A leader in the use of electroencephalography in epilepsy, he described the first nearly pathognomonic EEG pattern and demonstrated specific features for each of the three most common types of seizure. His pioneering investigations into the biochemical basis of epilepsy helped to identify pathological mechanisms in epileptic attacks. Lennox stood alone in his belief, now generally accepted, that the genetics of epilepsy could be understood only through a multifactorial mode of inheritance. The author presents an affectionate portrait of the physician, the teacher and the man, the founder of the Seizure Unit and the unifying force in the study of epilepsy by both professionals and lay persons.

Clinical Electroencephalography in Epilepsy and Related Conditions in Children

Clinical Electroencephalography in Epilepsy and Related Conditions in Children

Clinical Electroencephalography in Epilepsy and Related Conditions in Children

Here is an atlas composed of 223 reproductions of various types of abnormal electroencephalograms. It is introduced by 48 pages of brief discussion of technique and of the clinical entities and electrical patterns presented. Some are conditions found only in childhood; tracings used to illustrate the rest are those of children of different ages. The selection on epilepsies has been arranged roughly according to ages at which they appear, from hypsarrhythmia through petit mal, grand mal, cortical foci, psychomotor epilepsy, and vegetative seizures. The electroencephalographic correlates of cerebral palsy, acute convulsive encephalopathy, and febrile convulsions are presented. There is a comprehensive bibliography of approximately 400 items taken from European and American sources, chiefly between 1956 and 1961. The author is an assistant and docent of pediatrics at the University of Siena, Italy. His point of view is clinical and practical and this atlas is addressed primarily to the pediatrician and

Electroencephalography in Epilepsy

Electroencephalography in Epilepsy

Electroencephalography in Epilepsy

International Journal of Clinical PracticeVolume 4, Issue 11 p. 537-543 Article Electroencephalography in Epilepsy F. Graham Lescher M.C., M.D., F.R.C.P., F. Graham Lescher M.C., M.D., F.R.C.P. Physician, and Physician to the Neurological Department Derbyshire Royal InfirmarySearch for more papers by this author F. Graham Lescher M.C., M.D., F.R.C.P., F. Graham Lescher M.C., M.D., F.R.C.P. Physician, and Physician to the Neurological Department Derbyshire Royal InfirmarySearch for more papers by this author First published: 01 November 1950 https://doi.org/10.1111/j.1742-1241.1950.tb06020.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume4, Issue11November 1950Pages 537-543 RelatedInformation

Electroencephalography in epilepsy

Electroencephalography in epilepsy