Epileptic Electroencephalography Research Articles

Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures

Epilepsy is one of the most common neurological diseases globally. Notably, people in low to middle-income nations could not get proper epilepsy treatment due to the cost and availability of medical infrastructure. The risk of sudden unpredicted death in Epilepsy is considerably high. Medical statistics reveal that people with Epilepsy die more prematurely than those without the disease. Early and accurately diagnosing diseases in the medical field is challenging due to the complex disease patterns and the need for time-sensitive medical responses to the patients. Even though numerous machine learning and advanced deep learning techniques have been employed for the seizure stages classification and prediction, understanding the causes behind the decision is difficult, termed a black box problem. Hence, doctors and patients are confronted with the black box decision-making to initiate the appropriate treatment and understand the disease patterns respectively. Owing to the scarcity of epileptic Electroencephalography (EEG) data, training the deep learning model with diversified epilepsy knowledge is still critical. Explainable Artificial intelligence has become a potential solution to provide the explanation and result interpretation of the learning models. By applying the explainable AI, there is a higher possibility of examining the features that influence the decision-making that either the patient recorded from epileptic or non-epileptic EEG signals. This paper reviews the various deep learning and Explainable AI techniques used for detecting and predicting epileptic seizures using EEG data. It provides a comparative analysis of the different techniques based on their performance.

A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification

Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes.

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT.The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.

Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis.

Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold

The epileptic electroencephalography (EEG) classification technique has been extensively adopted in epilepsy diagnosis and management due to its powerful capacity in distinguishing brain dysfunction. Since EEG patterns vary significantly from patient to patient, conventional studies usually perform the training and testing processes on the same subject. However, the challenging issue regarding domain shift among different individuals remains unsolved, which leads to low popularization of clinical application. To alleviate such problem, a domain adaptation model is proposed in this paper. This method intends to learn the universal feature space between different patients via constructing an adversarial autoencoder. By performing an adversarial training procedure, the aggregated posterior of the embedding space is matched with a Riemannian manifold-based prior that contains cross-domain information. This variational inference process can increase the generalization ability while circumventing the overfitting to source domains. Moreover, a distance measure is developed to align the distributions among various domains using the Riemannian mean. It can make the extracted features present higher domain adaptability by minimizing domain gaps in hidden space. Two common classification tasks, seizure prediction and seizure detection, are utilized for model evaluation. In the tests performed on the CHB-MIT scalp EEG dataset, the proposed model achieves a sensitivity of 82.2%, a false alarm rate (FPR) of 0.13 h−1 for seizure prediction and a sensitivity of 86.4%, an FPR of 0.08 h−1 for seizure detection. Experimental results indicate that this method can effectively reduce the domain disparity compared with state-of-the-art baselines.

Quantitative Analysis of Inter- and Intrahemispheric Coherence on Epileptic Electroencephalography Signal.

When an epileptic seizure occurs, the neuron's activity of the brain is dynamically changed, which affects the connectivity between brain regions. The connectivity of each brain region can be quantified by electroencephalography (EEG) coherence, which measures the statistical correlation between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an energy-based channel selection process to determine the coherence analysis in the most active brain regions during the seizure. This paper presents a quantitative analysis of inter- and intrahemispheric coherence in epileptic EEG signals and the correlation with the channel activity to glean insights about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from ten patients’ data from the CHB-MIT dataset. Pair-wise electrode spectral coherence is calculated in the full band and five sub-bands of EEG signals. The channel activity level is determined by calculating the energy of each channel in all patients. The EEG coherence observation in the preictal (Cohpre) and ictal (Cohictal) conditions showed a significant decrease of Cohictal in the most active channel, especially in the lower EEG sub-bands. This finding indicates that there is a strong correlation between the decrease of mean spectral coherence and channel activity. The decrease of coherence in epileptic conditions (Cohictal <Cohpre) indicates low neuronal connectivity. There are some exceptions in some channel pairs, but a constant pattern is found in the high activity channel. This shows a strong correlation between the decrease of coherence and the channel activity. The finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable to be analyzed in the more active brain regions.

Subbands and cumulative sum of subbands based nonlinear features enhance the performance of epileptic seizure detection

We here proposed a novel fusion method of frequency slice wavelet transform (FSWT)-based fuzzy entropy (FuzzyEn) and Higuchi’s fractal dimension (HFD), t-distributed stochastic neighbor embedding (t-SNE) and K-nearest neighbor (KNN) to achieve the goal of automated detection of epileptic electroencephalography (EEG). Fifteen EEG subbands were first separated from the original EEG via FSWT, then their cumulative sum of subbands (CSoS) including forward CSoS and backward CSoS were computed. FuzzyEn and HFD were then employed to quantify the nonlinear properties of these subbands and CSoS, and followed by mapping the high-dimensional features into the two-dimensional space using t-SNE. Finally, the two-dimensional features were fed into KNN for classification. Eight binary-class, three ternary-class and one quinary-class cases, in total twelve classification tasks of the Bonn EEG database were conducted. The proposed approach attained accuracies of approximately 100% and 93.62% in the first 11 and 12th cases, respectively. Experimental results not only manifest our proposal is superior to most of existing methods, but also demonstrate subbands and CSoS based nonlinear features enhance the performance of epileptic seizure detection.

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals.

Synchronization phenomena of epileptic electroencephalography (EEG) have long been studied. In this study, we aim at investigating the spatial-temporal synchronization pattern in epileptic human brains using the spectral graph theoretic features extracted from scalp EEG and developing an efficient multivariate approach for detecting seizure onsets in real time. A complex network model is used for representing the recurrence pattern of EEG signals, based on which the temporal synchronization patterns are quantified using the spectral graph theoretic features. Furthermore, a statistical control chart is applied to the extracted features overtime for monitoring the transits from normal to epileptic states in multivariate EEG systems. Our method is tested on 23 patients from CHB-MIT Scalp EEG database. The results show that the graph theoretic feature yields a high sensitivity ( ∼98%) and low latency ( ∼6s) on average, and seizure onsets in 18 patients are 100% detected. Our approach validates the increased temporal synchronization in epileptic EEG and achieves a comparable detection performance to previous studies. We characterize the temporal synchronization patterns of epileptic EEG using spectral network metrics. In addition, we found significant changes in temporal synchronization in epileptic EEG, which enable a patient-specific approach for real-time seizure detection for personalized diagnosis and treatment.

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network.

Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly. We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays. Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags. The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation. The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.

Efficient Stepsize Selection Strategy for Givens Parametrized ICA Applied to EEG Denoising

In this letter, an efficient stepsize selection strategy for Givens parametrization-based independent component analysis (ICA) approaches is presented. The method is an extension of the pioneering deflationary ICA algorithm introduced by Delfosse and Loubaton (DelL $^{\mathbb{R}}$ ). The methodology underlying the computation of such a stepsize is based on sampling the contrast function using the information carried by its frequency components. Epileptic ElectroEncephaloGraphy (EEG)-based simulations show that the proposed approach stands for the best extraction accuracy/numerical complexity tradeoff with 15 times faster execution time. This is compared to a deterministic implementation of the DelL $^{\mathbb{R}}$ technique and its recently proposed Jacobi-based variant, namely the JDICA method.

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG.

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.

Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) record a mixture of ongoing neural processes, physiological and nonphysiological noise. The pattern of interest, such as epileptic activity, is often hidden within this noisy mixture. Therefore, blind source separation (BSS) techniques, which can retrieve the activity pattern of each underlying source, are very useful. Tensor decomposition techniques are very well suited to solve the BSS problem, as they provide a unique solution under mild constraints. Uniqueness is crucial for an unambiguous interpretation of the components, matching them to true neural processes and characterizing them using the component signatures. Moreover, tensors provide a natural representation of the inherently multidimensional EEG and fMRI, and preserve the structural information defined by the interdependencies among the various modes such as channels, time, patients, etc. Despite the well‐developed theoretical framework, tensor‐based analysis of real, large‐scale clinical datasets is still scarce. Indeed, the application of tensor methods is not straightforward. Finding an appropriate tensor representation, suitable tensor model, and interpretation are application dependent choices, which require expertise both in neuroscience and in multilinear algebra. The aim of this paper is to provide a general guideline for these choices and illustrate them through successful applications in epilepsy. WIREs Data Mining Knowl Discov 2017, 7:e1197. doi: 10.1002/widm.1197This article is categorized under: Algorithmic Development &gt; Biological Data Mining Algorithmic Development &gt; Spatial and Temporal Data Mining Algorithmic Development &gt; Structure Discovery

ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES

ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG

Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG

Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important problems have not yet been benefitted from DWT, including epileptic focus localization, a key task in epilepsy diagnosis and treatment. Additionally, the parameters and settings for DWT are chosen empirically or arbitrarily in previous work. In this work, we propose a framework to use DWT and support vector machine (SVM) for epileptic focus localization problem based on EEG. To provide a guideline in selecting the best settings for DWT, we decompose the EEG segments in seven commonly used wavelet families to their maximum theoretical levels. The wavelet and its level of decomposition providing the highest accuracy in each wavelet family are then used in a grid search for obtaining the optimal frequency bands and wavelet coefficient features. Our approach achieves promising performance on two widely-recognized intrancranial EEG datasets that are also seizure-free, with an accuracy of 83.07% on the Bern-Barcelona dataset and an accuracy of 88.00% on the UBonn dataset. Compared with existing DWT-based approaches in epileptic EEG analysis, the proposed approach leads to more accurate and robust results. A guideline for DWT parameter setting is provided at the end of the paper.

Epileptic Electroencephalography Profile Associates with Attention Problems in Children with Fragile X Syndrome: Review and Case Series.

Fragile X syndrome (FXS) is the most common cause of inherited intellectual disability and a variant of autism spectrum disorder (ASD). The FXS population is quite heterogeneous with respect to comorbidities, which implies the need for a personalized medicine approach, relying on biomarkers or endophenotypes to guide treatment. There is evidence that quantitative electroencephalography (EEG) endophenotype-guided treatments can support increased clinical benefit by considering the patient's neurophysiological profile. We describe a case series of 11 children diagnosed with FXS, aged one to 14 years, mean 4.6 years. Case data are based on longitudinal clinically-observed reports by attending physicians for comorbid symptoms including awake and asleep EEG profiles. We tabulate the comorbid EEG symptoms in this case series, and relate them to the literature on EEG endophenotypes and associated treatment options. The two most common endophenotypes in the data were diffuse slow oscillations and epileptiform EEG, which have been associated with attention and epilepsy respectively. This observation agrees with reported prevalence of comorbid behavioral symptoms for FXS. In this sample of FXS children, attention problems were found in 37% (4 of 11), and epileptic seizures in 45% (5 of 11). Attention problems were found to associate with the epilepsy endophenotype. From the synthesis of this case series and literature review, we argue that the evidence-based personalized treatment approach, exemplified by neurofeedback, could benefit FXS children by focusing on observable, specific characteristics of comorbid disease symptoms.

Fuzzy Mutual Information as a Dimensionality Reduction Technique for Epileptic Electroencephalography Signals

The aim of this study is to use Fuzzy Mutual Information as a Dimensionality Reduction Technique for Epileptic Electroencephalography Signals. To design an effective classification model, it is vital to extract a small set of closely related relevant features from a data set which has a high dimension. Such a type of procedure should explore the series of estimations of the relationship between each and every pair of variables. Also, the estimation is done between the two variables and also for the class labels too. For continuous and hybrid data there are various other strategies that are useful for the estimation of mutual information. Fuzzy Mutual Information is very helpful for obtaining the most stable feature sets and the relationships between two variables is accurately estimated. In this study, Fuzzy Mutual Information is applied as the dimensionality reduction technique for the electroencephalography signals obtained from epileptic patients. The Electroencephalogram (EEG) is actually a measure of the cumulative firing of neurons in various parts of the brain. The EEG contains the information with regard to the changes in the electrical potential of the brain which is obtained from a set of recording electrodes. Here the results are discussed using Fuzzy Mutual Information technique as a dimensionality reduction technique for the processing of electroencephalography signals from an epileptic patient.

WONOEP appraisal: optogenetic tools to suppress seizures and explore the mechanisms of epileptogenesis.

Optogenetics is a novel technology that combines optics and genetics by optical control of microbial opsins, targeted to living cell membranes. The versatility and the electrophysiologic characteristics of the light-sensitive ion-channels channelrhodopsin-2 (ChR2), halorhodopsin (NpHR), and the light-sensitive proton pump archaerhodopsin-3 (Arch) make these optogenetic tools potent candidates in controlling neuronal firing in models of epilepsy and in providing insights into the physiology and pathology of neuronal network organization and synchronization. Opsins allow selective activation of excitatory neurons and inhibitory interneurons, or subclasses of interneurons, to study their activity patterns in distinct brain-states in vivo and to dissect their role in generation of synchrony and seizures. The influence of gliotransmission on epileptic network function is another topic of great interest that can be further explored by using light-activated Gq protein-coupled opsins for selective activation of astrocytes. The ever-growing optogenetic toolbox can also be combined with emerging techniques that have greatly expanded our ability to record specific subtypes of cortical and hippocampal neurons in awake behaving animals such as juxtacellular recording and two-photon guided whole-cell recording, to identify the specific subtypes of neurons that are altered in epileptic networks. Finally, optogenetic tools allow rapid and reversible suppression of epileptic electroencephalography (EEG) activity upon photoactivation. This review outlines the most recent advances achieved with optogenetic techniques in the field of epilepsy by summarizing the presentations contributed to the 13th ILAE WONOEP meeting held in the Laurentian Mountains, Quebec, in June 2013.

Neonatal thalamic hemorrhage is strongly associated with electrical status epilepticus in slow wave sleep

Thalamic hemorrhage has been associated with neonatal cerebral sinovenous thrombosis (CSVT), especially when the straight sinus is involved, and often presents with neonatal seizures. Early thalamic injury has previously been shown to predispose to epilepsy and electrical status epilepticus in slow wave sleep (ESES). The objective of this study was to assess the prevalence of sleep-induced epileptic electroencephalography (EEG) abnormalities and postneonatal epilepsy after neonatal thalamic hemorrhage associated with CSVT, in the absence of more widespread cerebral damage. Between 2003 and 2008 15 neonates were diagnosed with a thalamic hemorrhage due to suspected or proven CSVT. Neurodevelopment and the history of seizures were assessed at follow-up in the outpatient clinic in all 14 survivors (age 2-9years). Whole-night or sleep-deprived EEG recordings were obtained to assess the prevalence of interictal epileptiform activity (EA) and calculate a sleep-induced spike and wave index (SWI). Three children were diagnosed with classic ESES (SWI >85%). Two children had ESES spectrum disorder (SWI between 50% and 85%), and in two children significant sleep-induced epileptiform activity (SIEA) was noted (SWI between 25% and 50%). Two other children were diagnosed with focal epilepsy, in the absence of sleep-induced epileptiform EEG abnormalities. Five children (age 2-7years) had normal EEG recordings at follow-up. Deficits in neurodevelopment were seen significantly more often in children with ESES, ESES spectrum, or SIEA. Neonates with thalamic hemorrhage associated with straight sinus thrombosis, without evidence of more widespread cerebral damage, are at high risk of developing ESES (spectrum) disorder (35%), SIEA (14%), or focal epilepsy (14%). Electrographic abnormalities may already be present prior to recognition of cognitive deficits. Early diagnosis may guide parents and caregivers, and subsequent treatment may improve neurodevelopmental outcome. Routine annual sleep EEG recordings in children with neonatal thalamic injury following CSVT may improve recognition of ESES.

Phase space approach for modeling of epileptic dynamics

Epileptic electroencephalography recordings can be described in terms of four prototypic wave forms: fast sinusoidal oscillations, large slow waves, fast spiking, and spike waves. On the macroscopic level, these wave forms have been modeled by different mechanistic models which share canonical features. Here we derive a minimal model of excitatory and inhibitory processes with features common to all previous models. We can infer that at least three interacting processes are required to support the prototypic epileptic dynamics. Based on a separation of time scales we analyze the model in terms of interacting manifolds in phase space. This allows qualitative reverse engineering of all epileptic wave forms and transitions between them. We propose this method as a complement to traditional approaches to modeling epileptiform rhythms.