Anti-seizure medication prescription preferences: a Moroccan multicenter study.

Background: The management of epilepsy is mainly based on antiseizure medications (ASMs). More than 20 ASMs have been introduced in clinical practice, providing a multitude of prescription choices. To date, there are no published data on the trends in ASMs prescriptions in Morocco. Therefore, we conducted a survey among practicing neurologists in seven tertiary referral hospitals in Morocco to study the current ASMs prescription preferences and their modifying factors. Methods: Our descriptive and analytical cross-sectional study was based on a survey sent between January and April 2022 to neurologists practicing in seven tertiary referral hospitals in Morocco. Information regarding the prescription of ASMs was collected using an exploitation form and analyzed using the SPSS version 13 software. Results: Based on questionnaire responses, our results showed that Valproic acid (96.3%) and Lamotrigine (59.8%) were the two most prescribed ASMs for generalized seizure types. For focal seizure types, Carbamazepine (98.8%) and Levetiracetam (34.1%) were the most commonly prescribed drugs, whereas for combined focal and generalized seizure types, the combination of Valproic acid and Carbamazepine (38.55%) was the most prescribed. Phenobarbital was the most commonly prescribed ASM for status epilepticus (40.2%). These prescription preferences were mainly due to seizure types, cost, health insurance coverage, years of experience, and additional epileptology training (p < 0.05). Conclusion: Our results show a shift in the prescription of ASMs in Morocco. Similar to many other countries, valproic acid and carbamazepine are considered the first-line treatments for generalized and focal seizure types. Some factors remain as major challenges in enhancing epilepsy management in Morocco.

Use of sedation-awakening electroencephalography in dogs with epilepsy.

BACKGROUND: Electroencephalography (EEG) recording protocols have been standardized for humans. Although the utilization of techniques in veterinary medicine is increasing, a standard protocol has not yet been established. HYPOTHESIS: Assessment of a sedation-awakening EEG protocol in dogs. ANIMALS: Electroencephalography examination was performed in a research colony of 6 nonepileptic dogs (control [C]) and 12 dogs with epilepsy admitted to the clinic because of the epileptic seizures. METHODS: It was a prospective study with retrospective control. Dogs with epilepsy were divided into 2 equal groups, wherein EEG acquisition was performed using a "sedation" protocol (IE-S, n = 6) and a "sedation-awakening" protocol (IE-SA, n = 6). All animals were sedated using medetomidine. In IE-SA group, sedation was reversed 5 minutes after commencing the EEG recording by injecting atipamezole IM. Type of background activity (BGA) and presence of EEG-defined epileptiform discharges (EDs) were evaluated blindly. Statistical significance was set at P > 0.05. RESULTS: Epileptiform discharges were found in 1 of 6 of the dogs in group C, 4 of 6 of the dogs in IE-S group, and 5 of 6 of the dogs in IE-SA group. A significantly greater number of EDs (spikes, P = .0109; polyspikes, P = .0109; sharp waves, P = .01) were detected in Phase 2 in animals subjected to the "sedation-awakening" protocol, whereas there was no statistically significant greater number of discharges in sedated animals. CONCLUSIONS AND CLINICAL IMPORTANCE: A "sedation-awakening" EEG protocol could be of value for ambulatory use if repeated EEG recordings and monitoring of epilepsy in dogs is needed.

Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals.

The visual scrutinization process for detecting epileptic seizures (ictal patterns) is time-consuming and prone to manual errors, which can have serious consequences, including drug abuse and life-threatening situations. To address these challenges, expert systems for automated detection of ictal patterns have been developed, yet feature engineering remains problematic due to variability within and between subjects. Single-objective optimization approaches yield less reliable results. This study proposes a novel expert system using the non-dominated sorting genetic algorithm (NSGA)-II to detect ictal patterns in brain signals. Employing an evolutionary multi-objective optimization (EMO) approach, the classifier minimizes both the number of features and the error rate simultaneously. Input features include statistical features derived from phase space transformations, singular values, and energy values of time-frequency domain wavelet packet transform coefficients. Through evolutionary transfer optimization (ETO), the optimal feature set is determined from training datasets and passed through a generalized regression neural network (GRNN) model for pattern detection of testing datasets. The results demonstrate high accuracy with minimal computation time (<0.5 s), and EMO reduces the feature set matrix by more than half, suggesting reliability for clinical applications. In conclusion, the proposed model offers promising advancements in automating ictal pattern recognition in EEG data, with potential implications for improving epilepsy diagnosis and treatment. Further research is warranted to validate its performance across diverse datasets and investigate potential limitations.

Epilepsy Treatment and Diagnosis Enhanced by Current Nanomaterial Innovations: A Comprehensive Review.

Epilepsy is a complex disease in the brain. Complete control of seizure has always been a challenge in epilepsy treatment. Currently, clinical management primarily involves pharmacological and surgical interventions, with the former being the preferred approach. However, antiepileptic drugs often exhibit low bioavailability due to inherent limitations such as poor water solubility and difficulty penetrating the blood-brain barrier (BBB). These issues significantly reduce the drugs' effectiveness and limit their clinical application in epilepsy treatment. Additionally, the diagnostic accuracy of current imaging techniques and electroencephalography (EEG) for epilepsy is suboptimal, often failing to precisely localize epileptogenic tissues. Accurate diagnosis is critical for the surgical management of epilepsy. Thus, there is a pressing need to enhance both the therapeutic outcomes of epilepsy medications and the diagnostic precision of the condition. In recent years, the advancement of nanotechnology in the biomedical sector has led to the development of nanomaterials as drug carriers. These materials are designed to improve drug bioavailability and targeting by leveraging their large specific surface area, facile surface modification, ability to cross the BBB, and high biocompatibility. Furthermore, nanomaterials have been utilized as contrast agents in imaging and as materials for EEG electrodes, enhancing the accuracy of epilepsy diagnoses. This review provides a comprehensive examination of current research on nanomaterials in the treatment and diagnosis of epilepsy, offering new strategies and directions for future investigation.

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks.

Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.

Consensus Guideline on the Management of Epilepsy in Egypt: A National Delphi Consensus Study.

OBJECTIVE: In epilepsy, early diagnosis, accurate determination of epilepsy type, proper selection of antiseizure medication, and monitoring are all essential. However, despite recent therapeutic advances and conceptual reconsiderations in the classification and management of epilepsy, serious gaps are still encountered in day-to-day practice in Egypt as well as several other resource-limited countries. Premature mortality, poor quality of life, socio-economic burden, cognitive problems, poor treatment outcomes, and comorbidities are major challenges that require urgent actions to be implemented at all levels. In recognition of this, a group of Egyptian epilepsy experts met through a series of consecutive meetings to specify the main concepts concerning the diagnosis and management of epilepsy, with the ultimate goal of establishing a nationwide Egyptian consensus. METHODS: The consensus was developed through a modified Delphi methodology. A thorough review of the most recent relevant literature and international guidelines was performed to evaluate their applicability to the Egyptian situation. Afterward, several remote and live rounds were scheduled to reach a final agreement for all listed statements. RESULTS: Of 278 statements reviewed in the first round, 256 achieved ≥80% agreement. Live discussion and refinement of the 22 statements that did not reach consensus during the first round took place, followed by final live voting then consensus was achieved for all remaining statements. SIGNIFICANCE: With the implementation of these unified recommendations, we believe this will bring about substantial improvements in both the quality of care and treatment outcomes for persons with epilepsy in Egypt. PLAIN LANGUAGE SUMMARY: This work represents the efforts of a group of medical experts to reach an agreement on the best medical practice related to people with epilepsy based on previously published recommendations while taking into consideration applicable options in resource-limited countries. The publication of this document is expected to minimize many malpractice issues and pave the way for better healthcare services on both individual and governmental levels.

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

Clinical characteristics and diagnoses of 1213 children referred to a first seizure clinic.

OBJECTIVE: New-onset seizure-like events (SLEs) are common in children, but differentiating between epilepsy and its mimics is challenging. This study provides an overview of the clinical characteristics, diagnoses, and corresponding etiologies of children evaluated at a first seizure clinic (FSC), which will be helpful for all physicians involved in the care of children with SLEs. METHODS: We included 1213 children who were referred to the FSC of a Dutch tertiary children's hospital over a 13-year period and described their clinical characteristics, first routine EEG recording results, and the distribution and specification of their eventual epilepsy and non-epilepsy diagnoses. The time interval to correct diagnosis and the diagnostic accuracy of the FSC were evaluated. RESULTS: "Epilepsy" was eventually diagnosed in 407 children (33.5%), "no epilepsy" in 737 (60.8%), and the diagnosis remained "unclear" in 69 (5.7%). Epileptiform abnormalities were seen in 60.9% of the EEG recordings in the "epilepsy" group, and in 5.7% and 11.6% of the "no epilepsy" and "unclear" group, respectively. Of all children with final "epilepsy" and "no epilepsy" diagnoses, 68.6% already received their diagnosis at FSC consultation, and 2.9% of the children were initially misdiagnosed. The mean time to final diagnosis was 2.0 months, and 91.3% of all children received their final diagnosis within 12 months after the FSC consultation. SIGNIFICANCE: We describe the largest pediatric FSC cohort to date, which can serve as a clinical frame of reference. The experience and expertise built at FSCs will improve and accelerate diagnosis in children with SLEs. PLAIN LANGUAGE SUMMARY: Many children experience events that resemble but not necessarily are seizures. Distinguishing between seizures and seizure mimics is important but challenging. Specialized first-seizure clinics can help with this. Here, we report data from 1213 children who were referred to the first seizure clinic of a Dutch children's hospital. One-third of them were diagnosed with epilepsy. In 68.8% of all children-with and without epilepsy-the diagnosis was made during the first consultation. Less than 3% were misdiagnosed. This study may help physicians in what to expect regarding the diagnoses in children who present with events that resemble seizures.

Video-EEG in the first 24 hours after the first unprovoked seizure in patients with normal neurological examination and head CT scan.

PURPOSE: to determine the yield of Video-Electroencephalogram (VEEG) in the first 24 h in patients with a first unprovoked seizure and normal neurological examination, laboratory findings, and cranial CT scans. METHODS: we analyzed retrospectively the yield of VEEG performed in these patients in the emergency department. All the patients were subsequently seen in the Epilepsy Clinic, and the epilepsy diagnosis was confirmed. RESULTS: we included 19 patients who met the inclusion criteria; all of them underwent VEEG with the 10-20 system within the first 24 h after the seizure. The duration of the recordings averaged at 108.53 min and may or may not have included intermittent photic stimulation and sleep recording; 74% of the recordings were abnormal, with 26% being normal. Among the abnormal cases, epileptogenic activity was found in 47% and seizures in 26% of the patients; because both findings could be present in the same VEEG, 63% of all the VEEG showed epileptogenic alterations or seizures. The VEEG anomalies were recorded before the 20th minute (standard VEEG duration) in 58% of patients who exhibited epileptogenic activity and/or seizures, and after the 20th minute in 42%. CONCLUSION: conducting approximately 100-minute VEEGs within the first 24 h after a first unprovoked seizure can enhance the diagnostic yield in patients with epilepsy. However, the study has the limitations of its sample size and retrospective nature.

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages.

Introduction: The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky. Methods: In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input. Results: Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat. Discussion: Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges.

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians' ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.

Scalp ripple rates for rapid epilepsy differentiation and seizure activity assessment: Applicability and influential factors.

OBJECTIVE: We aim to determine whether automatically detected ripple rate (ADRR) of 10-min scalp electroencephalography (EEG) during slow-wave sleep can be a useful tool for rapid epilepsy differentiation and seizure activity assessment, and we analyze the clinical factors that may affect the scalp ripple rates. METHODS: We retrospectively included 336 patients who underwent long-term video-EEG with a sampling rate ≥1000 Hz, and three groups were established based on their final clinical diagnosis (non-epilepsy; non-active epilepsy [epilepsy being seizure-free for at least 1 year]; and active epilepsy [epilepsy with one or more seizures in the past year]). ADRRs between groups were compared and diagnostic thresholds set according to the maximum of Youden index with the receiver-operating characteristic curve. RESULTS: The 336 patients comprised 49 non-epilepsy and 287 epilepsy patients (95 non-active epilepsy and 192 active epilepsy). The median ADRR of the epilepsy group was significantly greater than in the non-epilepsy group, with a diagnostic threshold of 4.25 /min (specificity 89.8%, sensitivity 47.74%, p<.001). Following stratification by age, the area under the curve was greatest in the 0-20 year subgroup, threshold 4.10 /min (specificity 100%, sensitivity 52.47%, p<.001). Regarding distinguishing active epilepsy from non-active epilepsy patients, the area under the curve was also greatest in patients 0-20 years of age, threshold 13.05/min (specificity 98.36%, sensitivity 35.64%, p<.001). Following stratification by epilepsy type, the diagnostic efficiency was best in children with developmental and epileptic encephalopathies/epileptic encephalopathies (DEEs/EEs) (threshold 5.20/min, specificity 100%, sensitivity 100%) and self-limited focal epilepsies (SeLFEs) (threshold 5.45/min, specificity 80%, sensitivity 100%). Multivariate analysis revealed that the influential factors of ADRRs were age, depth of epileptogenic lesion, and seizure frequency. SIGNIFICANCE: ADRR of scalp EEG can be a rapid and specific method to differentiate epilepsy and evaluate seizure activity. This method is especially suitable for young patients.

On epilepsy perception: Unravelling gaps and issues.

Epilepsy is one of the most common neurological diseases, but it can sometimes be under-reported or have a time delay in diagnosis. This data is not surprising if we consider that a person often seeks medical attention only after presenting a generalized tonic-clonic seizure. Epilepsy diagnostic delay is caused by several factors: under-reporting by patients, under-diagnosed epileptic manifestations by inexperienced clinicians, and lack of time in the emergency setting. The consequences of this delay are increased accidents, a high rate of premature mortality, and economic expanses for the healthcare system. Moreover, people with epilepsy have a higher probability of comorbidities than the general population, such as mood disorders or cognitive problems. Along with recurrent seizures, these comorbid diseases promote isolation and stigmatization of people with epilepsy, who suffer from discrimination at school, in the workplace, and even in social relationships. Public awareness of epilepsy and its comorbidities is necessary to prevent diagnostic delays and overcome social and professional iniquities for people with epilepsy.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs.

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG.

This study investigates the performance of a convolutional neural network (CNN) algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. A CNN algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 s of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. The trained CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1 score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis. 1: EEG measurements and subsequent connectivity calculation, 2: training of a neural network on resulting connectivity matrices, 3: extraction of most efficient CNN filters, which are neuromarker for epilepsy.

Diagnostic Delay in Epilepsy: the Scope of the Problem.

PURPOSE OF REVIEW: Diagnostic delay is an increasingly recognized issue in epilepsy. At the same time, there is a clear disparity between public awareness of epilepsy and that of other public health issues. A contributing factor for this seems to be a lack of studies testing interventions designed to improve seizure recognition. In this review, we summarize the main findings from recent studies investigating diagnostic delay in epilepsy, highlighting causes, consequences, and potential interventions in future research that may improve quality of care in this population. RECENT FINDINGS: Building on prior evidence, diagnostic delay in patients with new-onset focal epilepsy has been identified as an important problem for patients with epilepsy. Such delay in diagnosis can lead to delayed treatment and potentially preventable morbidity and mortality including motor vehicle accidents. Nonmotor seizure semiology appears to be a major contributor for delay; such seizures are largely unrecognized when patients present to emergency departments for care. Improving recognition and diagnosis of recurrent nonmotor seizures in emergency departments represents a significant opportunity for improving time to diagnosis, particularly when patients present following a first lifetime motor seizure and meet diagnostic criteria for epilepsy. Diagnostic delay in epilepsy is a significant public health issue and recent studies have highlighted potential areas for intervention.

EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks.

Epilepsy is a brain abnormality that leads its patients to suffer from seizures, which conditions their behavior and lifestyle. Neurologists use an electroencephalogram (EEG) to diagnose this disease. This test illustrates the signaling behavior of a person's brain, allowing, among other things, the diagnosis of epilepsy. From a visual analysis of these signals, neurologists identify patterns such as peaks or valleys, looking for any indication of brain disorder that leads to the diagnosis of epilepsy in a purely qualitative way. However, by applying a test based on Fourier signal analysis through rapid transformation in the frequency domain, patterns can be quantitatively identified to differentiate patients diagnosed with the disease and others who are not. In this article, an analysis of the EEG signal is performed to extract characteristics in patients already classified as epileptic and non-epileptic, which will be used in the training of models based on classification techniques such as logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Based on the results obtained with each technique, an analysis is performed to decide which of these behaves better. In this study traditional classification techniques were implemented that had as data frequency data in the channels with distinctive information of EEG examinations, this was done through a feature extraction obtained with Fourier analysis considering frequency bands. The techniques used for classification were implemented in Python and through a comparison of metrics and performance, it was concluded that the best classification technique to characterize epileptic patients are artificial neural networks with an accuracy of 86%.

A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.

OBJECTIVE: For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). METHODS: The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. RESULTS: We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. CONCLUSIONS: The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. SIGNIFICANCE: The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.