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BACKGROUND: Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG). HYPOTHESIS/OBJECTIVES: Characterize epilepsy in Egyptian Arabian foals with JIE using EEG. ANIMALS: Sixty-nine foals (JIE, 48; controls, 21). METHODS: Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age-matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls. RESULTS: Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals. CONCLUSIONS AND CLINICAL IMPORTANCE: Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic-clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self-limiting disorder might serve as a naturally occurring animal model for self-limited epilepsy in children.

Background: Déjà vu, French for “already seen,” is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as “non-ictal déjà vu”) and in epilepsy patients during seizures (as “ictal déjà vu”) and between seizures (as “interictal déjà vu”). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients. Methods: This study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/ excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence’s of the phenomena. Study quality, heterogeneity, and publication bias were assessed. Results: Database searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p  <  0.001) of healthy individuals, whereas interictal déjà vu was experienced by 0.62 (95% CI [0.48, 0.75], p  =  0.0 9 9 ) and ictal déjà vu by 0.22 (95% CI [0.15, 0.32], p  =  0.001) of epilepsy patients. Examinations of phenomenological (sex, age, frequency, duration, emotional valence, and dissociative symptoms) and neuroscientific (brain structures and functions) data revealed significant variations between non-ictal, interictal and ictal déjà vu on several domains. Conclusion: This systematic review and meta-analysis do not support the notion that non-ictal, interictal and ictal déjà vu are homogenous experiences. Instead, it provides insight into ictal déjà vu as a symptom of epilepsy that should be considered included in future revisions of the ICD-11. [ABSTRACT FROM AUTHOR]

Abstract Background Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG). Hypothesis/Objectives Characterize epilepsy in Egyptian Arabian foals with JIE using EEG. Animals Sixty‐nine foals (JIE, 48; controls, 21). Methods Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age‐matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls. Results Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals. Conclusions and Clinical Importance Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic‐clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self‐limiting disorder might serve as a naturally occurring animal model for self‐limited epilepsy in children.

Migraine Disease (MD) is one of the common primary headaches that can prevent patients from their everyday life. Despite the high prevalence, the pathophysiology of the disease has not been clearly understood yet. Here, the brain is considered as a dynamical system. The Chua's circuit with a chaotic attractor is the proposed model. This attractor has a one-scroll mode representing a healthy brain and a double-scroll mode representing a migraine sufferer brain. We believe that MD and Chua's systems have certain behavioral similarities. The boundaries of the attractor are the sensitive brain areas in which any small trigger can start the ictal phase of the migraine. The transition from the inter-ictal phase to the ictal phase in migraine patients occurs due to a decrease in serotonin levels when the brain is within the boundaries of the first attractor. Here, this is the results of the increase of system parameters. In addition, the transition from the ictal phase to the inter-ictal phase in a migraine sufferer brain is caused by a disruption of coordination in the brain's structures and this lasts for a certain period for every migraine patient. The structures which are the result of the Migraine Generator Network (MGN) and Cortical Spreading Depression (CSD). This explanation may propose newer methods for preventing or curing MD. To better understand MD to control it and shrink the areas involved in this disease, it is better to know the dynamic systems better. It may help prevent the formation of migraine ictal attractor or even make the migraine ictal phase attractor smaller even after it has been formed. [ABSTRACT FROM AUTHOR]

Video-electroencephalographic monitoring (VEM) is the gold standard for evaluating patients with epilepsy and seizures. The epilepsy monitoring unit (EMU) provides an environment in which to examine patients' seizures using VEM, to diagnose and characterise events/seizures, and to conduct a presurgical evaluation. Seizure observation and assessment are crucial roles for clinical nurses working in the EMU. However, different institutions use different seizure assessment tools. This lack of standardisation complicates the provision of optimal care for patients with seizures. This article aims to synthesise current recommendations regarding the components of seizure assessment and describe how they have been organised to form a standardised assessment tool. [ABSTRACT FROM AUTHOR]

Background: Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG). Hypothesis/Objectives: Characterize epilepsy in Egyptian Arabian foals with JIE using EEG. Animals: Sixty‐nine foals (JIE, 48; controls, 21). Methods: Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age‐matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls. Results: Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals. Conclusions and Clinical Importance: Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic‐clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self‐limiting disorder might serve as a naturally occurring animal model for self‐limited epilepsy in children. [ABSTRACT FROM AUTHOR]

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed preprocessing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages. The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results. [ABSTRACT FROM AUTHOR]

BackgroundDéjà vu, French for “already seen,” is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as “non-ictal déjà vu”) and in epilepsy patients during seizures (as “ictal déjà vu”) and between seizures (as “interictal déjà vu”). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients.MethodsThis study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence’s of the phenomena. Study quality, heterogeneity, and publication bias were assessed.ResultsDatabase searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p

Epilepsy is a common neurological disease characterized by seizures. A person with a seizure onset can lose consciousness which in turn can lead to fatal accidents. Electroencephalogram (EEG) is a recording of the electrical signals from the brain which is used to analyse the epileptic seizures. Physical visual examination of the EEG by trained neurologists is subjective and highly difficult due to the non-linear complex nature of the EEG. This opens a window for automatic detection of epileptic seizures using machine learning methods. In this work, we have used a standard database that consists of five different sets of EEG data including the epileptic EEG. Using this data, we have devised a novel 22 possible clinically significant cases with the combination of binary and multi class type of classification problem to automatically classify epileptic EEG. As the EEG is non-linear, we have devised 11 statistically significant non-linear entropy features to extract from this database. These features are fed to 10 different classifiers of various types for each of the 22 clinically significant cases and their classification accuracy is reported for 10-fold cross validation. Random Forest and Optimized Forest classifiers reported accuracies above 90% for all 22 cases considered in this study. Such vast possible clinically significant 22 cases from the combination of the data from the database considered has not been in the literature with the best of the knowledge of the authors. Comparing with the literature, several studies have presented one or few combinations of these 22 cases in this work. In comparison to similar works, the accuracies obtained by the classifiers were highly competitive. In addition, a novel integrated epilepsy detection index named EpilepIndex (IED) is able to differentiate between epileptic EEG and a normal EEG with 100% accuracy.

In this work, we investigate epilepsy seizure detection using machine learning. In the literature, a machine learning model is usually trained to help automate the epileptic detection process, eliminating the need for human intervention. Typically, the dataset is split into training and test sets in a way to maximize the detection accuracy. This requires the training set to include enough EEG samples for every possible patient in order to improve the accuracy numbers during the prediction. However, this might not be easy or practical in real life. A new patient might not have a previous record in the training set, and hence, the prediction for this particular patient might not meet the expected accuracy. The main contribution in this work is to study the impact of the training and test datasets selection from practical point of view on the accuracy and efficacy of the CNN prediction. In this work, a CNN model, namely AlexNet, is trained to detect epileptic states, namely preictal, interictal and ictal, in subjects using electroencephalogram (EEG) signals. The dataset includes the three epileptic zones of subjects taken from three medical centers, collected by the Fragility Multi-Center Retrospective Study. Furthermore, we propose a framework to utilize a feature extraction technique that exploits the available multiple channels of EEG signals to minimize information loss. As part of the main contribution, three different approaches are proposed to split the EEG sample dataset into the training and test sets. Thus, the prediction performance is evaluated based on the prior knowledge extracted from the particular samples picked for the training set. The results show that the proposed framework achieves an overall accuracy of 94.44% when the training contained samples from each patient. The accuracy is reduced to 92.98% when the training set contained a subset of the patient pool. A binary classification is also performed with up to 98% accuracy for both scenarios. [ABSTRACT FROM AUTHOR]

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiformpatterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the preictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures. [ABSTRACT FROM AUTHOR]

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

Purpose: The electrophysiological characterization of resting state oscillatory functional connectivity within the default mode network (DMN) during interictal periods in childhood absence epilepsy (CAE) remains unclear. Using magnetoencephalographic (MEG) recordings, this study investigated how the connectivity within the DMN was altered in CAE. Methods: Using a cross-sectional design, we analyzed MEG data from 33 children newly diagnosed with CAE and 26 controls matched for age and sex. The spectral power and functional connectivity of the DMN were estimated using minimum norm estimation combined with the Welch technique and corrected amplitude envelope correlation. Results: Default mode network showed stronger activation in the delta band during the ictal period, however, the relative spectral power in other bands was significantly lower than that in the interictal period (pcorrected < 0.05 for DMN regions, except bilateral medial frontal cortex, left medial temporal lobe, left posterior cingulate cortex in the theta band, and the bilateral precuneus in the alpha band). It should be noted that the significant power peak in the alpha band was lost compared with the interictal data. Compared with controls, the interictal relative spectral power of DMN regions (except bilateral precuneus) in CAE patients was significantly increased in the delta band (pcorrected < 0.01), whereas the values of all DMN regions in the beta-gamma 2 band were significantly decreased (pcorrected < 0.01). In the higher frequency band (alpha-gamma1), especially in the beta and gamma1 band, the ictal node strength of DMN regions except the left precuneus was significantly higher than that in the interictal periods (pcorrected < 0.01), and the node strength of the right inferior parietal lobe increased most significantly in the beta band (Ictal: 3.8712 vs. Interictal: 0.7503, pcorrected < 0.01). Compared with the controls, the interictal node strength of DMN increased in all frequency bands, especially the right medial frontal cortex in the beta band (Controls: 0.1510 vs. Interictal: 3.527, pcorrected < 0.01). Comparing relative node strength between groups, the right precuneus in CAE children decreased significantly (b: Controls: 0.1009 vs. Interictal: 0.0475; g 1: Controls:0.1149 vs. Interictal:0.0587, pcorrected < 0.01) such that it was no longer the central hub. Conclusion: These findings indicated DMN abnormalities in CAE patients, even in interictal periods without interictal epileptic discharges. Abnormal functional connectivity in CAE may reflect abnormal anatomo-functional architectural integration in DMN, as a result of cognitive mental impairment and unconsciousness during absence seizure. Future studies are needed to examine if the altered functional connectivity can be used as a biomarker for treatment responses, cognitive dysfunction, and prognosis in CAE patients. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder; 1% of people worldwide have epilepsy. Differentiating epileptic seizures from other acute neurological disorders in a clinical setting can be challenging. Approximately one-third of patients have drug-resistant epilepsy that is not well controlled by current antiepileptic drug therapy. Surgical treatment is potentially curative if the epileptogenic focus is accurately localized. Diffusion-weighted imaging (DWI) is an advanced magnetic resonance imaging technique that is sensitive to the diffusion of water molecules and provides additional information on the microstructure of tissue. Qualitative and quantitative analysis of peri-ictal, postictal, and interictal diffusion images can aid the differential diagnosis of seizures and seizure foci localization. This review focused on the fundamentals of DWI and its associated techniques, such as apparent diffusion coefficient, diffusion tensor imaging, and tractography, as well as their impact on epilepsy in terms of differential diagnosis, epileptic foci determination, and prognosis prediction. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1–99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation. [ABSTRACT FROM AUTHOR]

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

Abstract Objective Standardized investigation of epileptic seizures and the postictal state may contribute to a better understanding of ictal and postictal phenomena. This comparative case study aims to assess whether electrically induced seizures in electroconvulsive therapy (ECT) show sufficient similarities with spontaneous seizures to serve as a human epilepsy model. Methods We compared six EEG recordings, three ECT‐induced seizures and three generalized tonic‐clonic seizures, using quantitative electroencephalography (EEG) analyses. EEG recordings during and after ECT sessions (under general anesthesia and muscle paralysis) were collected prospectively, whereas epilepsy data were selected retrospectively. Time‐frequency representations, dominant ictal frequencies, and postictal alpha‐delta ratios were calculated. Results In all EEG recordings, a decrease in dominant ictal frequency was observed, as well as postictal suppression. Postictal alpha‐delta ratio indicated the same trend for all: a gradual increase from predominantly delta to alpha frequencies on timescales of hours after the seizure. Postictal spectral representation was similar. Muscle artifacts were absent in ECT‐induced seizures and present in spontaneous seizures. Ictal amplitude was higher in epileptic than in ECT‐induced seizures. Temporospectral ictal dynamics varied slightly between groups. Significance We show that ictal and postictal characteristics in ECT and patients with generalized tonic‐clonic seizures are essentially similar. ECT‐induced seizures may be used to investigate aspects of ictal and postictal states in a highly predictable manner and well‐controlled environment. This suggests that clinical and electrophysiological observations during ECT may be extrapolated to epilepsy with generalized tonic‐clonic seizures.

Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications. [ABSTRACT FROM AUTHOR]

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

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat'z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods. • Obtained competing evaluation score relative to SOTA methods using ML methods. • Competing score reported for interictal-preictal based classification of EEG data. • EEG classification is performed in the sensing time domain itself. • The proposed approach is validated using three benchmark datasets. • The performance is evaluated using cross-validation technique. [ABSTRACT FROM AUTHOR]

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data. [ABSTRACT FROM AUTHOR]

Objective: Standardized investigation of epileptic seizures and the postictal state may contribute to a better understanding of ictal and postictal phenomena. This comparative case study aims to assess whether electrically induced seizures in electroconvulsive therapy (ECT) show sufficient similarities with spontaneous seizures to serve as a human epilepsy model. Methods: We compared six EEG recordings, three ECT‐induced seizures and three generalized tonic‐clonic seizures, using quantitative electroencephalography (EEG) analyses. EEG recordings during and after ECT sessions (under general anesthesia and muscle paralysis) were collected prospectively, whereas epilepsy data were selected retrospectively. Time‐frequency representations, dominant ictal frequencies, and postictal alpha‐delta ratios were calculated. Results: In all EEG recordings, a decrease in dominant ictal frequency was observed, as well as postictal suppression. Postictal alpha‐delta ratio indicated the same trend for all: a gradual increase from predominantly delta to alpha frequencies on timescales of hours after the seizure. Postictal spectral representation was similar. Muscle artifacts were absent in ECT‐induced seizures and present in spontaneous seizures. Ictal amplitude was higher in epileptic than in ECT‐induced seizures. Temporospectral ictal dynamics varied slightly between groups. Significance: We show that ictal and postictal characteristics in ECT and patients with generalized tonic‐clonic seizures are essentially similar. ECT‐induced seizures may be used to investigate aspects of ictal and postictal states in a highly predictable manner and well‐controlled environment. This suggests that clinical and electrophysiological observations during ECT may be extrapolated to epilepsy with generalized tonic‐clonic seizures. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder; 1% of people worldwide have epilepsy. Differentiating epileptic seizures from other acute neurological disorders in a clinical setting can be challenging. Approximately one-third of patients have drug-resistant epilepsy that is not well controlled by current antiepileptic drug therapy. Surgical treatment is potentially curative if the epileptogenic focus is accurately localized. Diffusion-weighted imaging (DWI) is an advanced magnetic resonance imaging technique that is sensitive to the diffusion of water molecules and provides additional information on the microstructure of tissue. Qualitative and quantitative analysis of peri-ictal, postictal, and interictal diffusion images can aid the differential diagnosis of seizures and seizure foci localization. This review focused on the fundamentals of DWI and its associated techniques, such as apparent diffusion coefficient, diffusion tensor imaging, and tractography, as well as their impact on epilepsy in terms of differential diagnosis, epileptic foci determination, and prognosis prediction.

Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task. [ABSTRACT FROM AUTHOR]

This study evaluates the use of multiscale signal analysis to detect and predict seizures by finding the ictal and pre-ictal condition in electroencephalography (EEG) recordings. There are three processing stages in this study. The first is to decompose EEG signals by using empirical mode decomposition (EMD) and a coarse-grained (CG) procedure to obtain signal information in multiple scales. The second is extracting the features by calculating the fractal dimension of the decomposed signals. Eventually, k-NN, Random Forest, and support vector machine (SVM) classifiers are used to classify ictal and pre-ictal conditions. We evaluate the system using a public dataset from Bonn University. The combination of EMD with five IMFs, FD, and SVM is used for seizure detection (normal vs. ictal) and the three-class problem (normal vs. pre-ictal vs. ictal). The accuracy for seizure detection is 100%. For the three-class problem, we achieved a highest accuracy of 99.7%, and sensitivity and specificity of 99.7% and 99.9%, respectively. The combination of CG, FD, and SVM is proposed to predict a seizure (normal vs. pre-ictal) and achieves a maximum classification accuracy from 99.3% to 100%. These results indicate that the use of EMD with five IMFs is suitable for detecting seizures, while CG is suitable for predicting seizures in EEG signals.

Background: Electric and magnetic source imaging methods (ESI, MSI) estimate the location in the brain of the sources generating the interictal epileptiform discharges (II-ESI, II-MSI) and the ictal activity (IC-ESI, IC-MSI). These methods provide potentially valuable clinical information in the presurgical evaluation of patients with drug-resistant focal epilepsy, evaluated for surgical therapy. In spite of the significant technical advances in this field, and the numerous papers published on clinical validation of these methods, ESI and MSI are still underutilized in most epilepsy centers performing a presurgical evaluation. Our goal was to review and summarize the published evidence on the diagnostic accuracy of interictal and ictal ESI and MSI in epilepsy surgery.Methods: We searched the literature for papers on ESI and MSI that specified the diagnostic reference standard as the site of resection and the postoperative outcome (seizure-freedom). We extracted data from the selected studies, to calculate the diagnostic accuracy measures.Results: Our search resulted in 797 studies; 48 studies fulfilled the selection criteria (25 ESI and 23 MSI studies), providing data from 1,152 operated patients (515 for II-ESI, 440 for II-MSI, 159 for IC-ESI, and 38 for IC-MSI). The sensitivity of source imaging methods was between 74 and 90% (highest for IC-ESI). The specificity of the source imaging methods was between 20 and 54% (highest for II-MSI). The overall accuracy was between 50 and 75% (highest for IC-ESI). Diagnostic Odds Ratio was between 0.8 (IC-MSI) and 4.02–7.9 (II-ESI < II-MSI < IC-ESI).Conclusions: Our systematic review and meta-analysis provides evidence for the accuracy of source imaging in presurgical evaluation of patients with drug-resistant focal epilepsy. These methods have high sensitivity (up to 90%) and diagnostic odds ratio (up to 7.9), but the specificity is lower (up to 54%). ESI and MSI should be included in the multimodal presurgical evaluation.

Objective: To assess pharmacokinetics and safety of diazepam nasal spray (NRL‐1; VALTOCO®) in pediatric and adult patients with epilepsy in seizure and nonseizure states. Methods: A single dose of diazepam nasal spray (5, 10, 15, or 20 mg based on weight) was administered during each of two conditions (ictal/peri‐ictal and interictal condition) to patients 6‐65 years old with partial or generalized epilepsy with motor seizures or seizures with clear alteration of awareness; a second dose was permitted if needed for persistent seizures. Dosing could be interictal or ictal/peri‐ictal first, with a washout of ≥14 days. Blood samples for pharmacokinetic analysis were taken at prespecified time points. Treatment‐emergent adverse events (TEAEs), sedation, nasal irritation, nasal mucosal pain, and olfactory changes were assessed. Results: Of 57 patients in the study (mean age = 28.1 years [range = 6‐59], 54.4% female, 80.7% white), 49 were included in the primary pharmacokinetic analyses. Diazepam pharmacokinetic profiles were similar under both conditions, with approximately 2‐hour median time to mean (SD) maximum plasma concentrations of 164 (88) and 189 (110) ng/mL for ictal/peri‐ictal and interictal conditions, respectively; drug exposure during the first 6 hours postdosing was 532 (313) and 615 (368) h•ng/mL, respectively. Seventeen patients (29.8%) reported TEAEs, of whom eight (14%) had treatment‐related TEAEs, with those reported in ≥2 patients being dysgeusia (n = 3, 5.3%) and nasal discomfort (n = 2, 3.5%). One patient had serious TEAEs (recurrent seizures, metabolic encephalopathy), which were deemed unrelated to study treatment. No changes in respiratory rate were observed, nor were there clinically relevant changes in sedation, olfaction, nasal irritation, or acute nasal mucosal pain. Significance: The epileptic conditions (ictal/peri‐ictal, interictal) had minimal impact on diazepam nasal spray pharmacokinetics in patients with epilepsy. Therefore, diazepam nasal spray can be administered ictally and interictally. Diazepam nasal spray safety was consistent with the profile of diazepam. [ABSTRACT FROM AUTHOR]

Psychiatric disorders represent common comorbidities in epileptic patients. Sometimes anxiety is part of the ictal semiology, especially during seizures arising from/involving frontal or temporal lobes. We describe a patient with focal epilepsy and recurrent hyperkinetic seizures who also presented prolonged episodes characterized by massive anxiety, alarm and fear. A Video-Electroencephalographic monitoring performed during one of these attacks revealed a continuous epileptiform activity over the right frontal regions, consistent with a focal non-convulsive status epilepticus accounting for the patient's psychiatric symptoms. Our case confirms the complex relationship between epilepsy and anxiety. A review of the literature is also included. [ABSTRACT FROM AUTHOR]

Abstract Background Migraineurs seem to have cyclic variations in cortical excitability in several neurophysiological modalities. Laser-evoked potentials (LEP) are of particular interest in migraine because LEP specifically targets pain pathways, and studies have reported different LEP-changes both between and during headaches. Our primary aim was to explore potential cyclic variations in LEP amplitude and habituation in more detail with a blinded longitudinal study design. Methods We compared N1 and N2P2 amplitudes and habituation between two blocks of laser stimulations to the dorsal hand, obtained from 49 migraineurs with four sessions each. We used migraine diaries to categorize sessions as interictal (> one day from previous and to next attack), preictal (< one day before the attack), ictal or postictal (< one day after the attack). Also, we compared 29 interictal recordings from the first session to 30 controls. Results N1 and N2P2 amplitudes and habituation did not differ between preictal, interictal and postictal phase sessions, except for a post hoc contrast that showed deficient ictal habituation of N1. Habituation is present and similar in migraineurs in the interictal phase and controls. Conclusions Hand-evoked LEP amplitudes and habituation were mainly invariable between migraine phases, but this matter needs further study. Because hand-evoked LEP-habituation was similar in migraineurs and controls, the present findings contradict several previous LEP studies. Pain-evoked cerebral responses are normal and show normal habituation in migraine.

Background: Electric and magnetic source imaging methods (ESI, MSI) estimate the location in the brain of the sources generating the interictal epileptiform discharges (II-ESI, II-MSI) and the ictal activity (IC-ESI, IC-MSI). These methods provide potentially valuable clinical information in the presurgical evaluation of patients with drug-resistant focal epilepsy, evaluated for surgical therapy. In spite of the significant technical advances in this field, and the numerous papers published on clinical validation of these methods, ESI and MSI are still underutilized in most epilepsy centers performing a presurgical evaluation. Our goal was to review and summarize the published evidence on the diagnostic accuracy of interictal and ictal ESI and MSI in epilepsy surgery. Methods: We searched the literature for papers on ESI and MSI that specified the diagnostic reference standard as the site of resection and the postoperative outcome (seizure-freedom). We extracted data from the selected studies, to calculate the diagnostic accuracy measures. Results: Our search resulted in 797 studies; 48 studies fulfilled the selection criteria (25 ESI and 23 MSI studies), providing data from 1,152 operated patients (515 for II-ESI, 440 for II-MSI, 159 for IC-ESI, and 38 for IC-MSI). The sensitivity of source imaging methods was between 74 and 90% (highest for IC-ESI). The specificity of the source imaging methods was between 20 and 54% (highest for II-MSI). The overall accuracy was between 50 and 75% (highest for IC-ESI). Diagnostic Odds Ratio was between 0.8 (IC-MSI) and 4.02–7.9 (II-ESI < II-MSI < IC-ESI). Conclusions: Our systematic review and meta-analysis provides evidence for the accuracy of source imaging in presurgical evaluation of patients with drug-resistant focal epilepsy. These methods have high sensitivity (up to 90%) and diagnostic odds ratio (up to 7.9), but the specificity is lower (up to 54%). ESI and MSI should be included in the multimodal presurgical evaluation. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disease condition in which one experience seizures. These can be recorded using Electroencephalogram which is by nature long term recordings. A lot of work has been done towards automatic detection of these seizures in the literature with both short term and long term recordings. In this work, we have used the standard database of University of Bonn for a four different two class classification problem as addressed by various others in the literature. We have used novel single feature namely Tsallis entropy along with five different classifiers. Comparing with other literatures, we find that our method has the least computation time as low as 0.9 ms. We achieved a highest accuracy of 92.67–100% with Decision tree classifier for the four types of two class classification problem considered. Our method being very simple and also has fastest computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures. [ABSTRACT FROM AUTHOR]

Purpose Of Review: To summarize the literature regarding the prevalence, pathophysiology, and anatomic networks involved with painful seizures, which are a rare but striking clinical presentation of epilepsy.Recent Findings: Several recent large case series have explored the prevalence of the main cephalgic, somatosensory, and abdominal variants of this rare disorder. Research studies including the use of electrical stimulation and functional neuroimaging have demonstrated the networks underlying painful somatosensory or visceral seizures. Improved understanding of some of the overlapping mechanisms between migraines and seizures has elucidated their common pathophysiology. The current literature reflects a widening range of awareness and understanding of painful seizures, despite their rarity. [ABSTRACT FROM AUTHOR]