Validation of the Seizure-Related Impact Assessment Scale (SERIAS): a study protocol.

INTRODUCTION: This study aims to validate the Seizure-Related Impact Assessment Scale (SERIAS). This novel patient-reported outcome measure (PROM) compares the 'trade-off' between seizures and treatment-related adverse effects, and measures epilepsy disability qualitatively and quantitively. It fills an important gap in PROMs for epilepsy clinical trials and practice. METHODS AND ANALYSIS: Adults with epileptologist-confirmed epilepsy from two Australian Epilepsy Centres are being recruited. People with functional seizures, or who are unable to self-complete English-language validated instruments are excluded. Participants providing informed consent are invited to complete questionnaires at baseline, 3 and 6 months later. SERIAS includes five questions that ask about the number of days per month that seizures or treatment-related adverse effects partially or fully impact work/home/school and family/social/non-work activities, as well as a visual analogue scale regarding epilepsy-related disability. SERIAS is completed alongside seven internationally validated instruments measuring treatment-related adverse effects, mood disorders and quality of life. Target recruitment is n=100, ensuring>50 people complete all questionnaires at all timepoints. Comprehensive psychometric analysis will be performed. Convergent validity will be investigated using bivariate correlations with relevant measures. Reliability will be investigated using Cronbach's alpha, McDonald's omega and test-retest correlation coefficients. SERIAS will be a novel PROM for epilepsy clinical trials and practice. ETHICS AND DISSEMINATION: Multisite ethics approval was granted by the Alfred Health Ethics Committee (HREC 17/23). Results of this study will be disseminated through publication in peer-reviewed journals and presentations at scientific conferences. TRIAL REGISTRATION NUMBER: ACTRN12623000599673.

[Differential diagnosis of epileptogenic substrates of unknown etiology using a modification of the routine MRI protocol]. / Differentsial'naya diagnostika epileptogennykh substratov neyasnoi etiologii s pomoshch'yu modifikatsii protokola standartnoi MRT.

OBJECTIVE: To evaluate the diagnostic capabilities of modifying the standard MRI protocol as part of an interdisciplinary presurgical examination of patients with epileptogenic substrates of unknown etiology. MATERIAL AND METHODS: The results of dynamic MRI of 8 patients with a referral diagnosis of focal cortical dysplasia (FCD) were analyzed. In 7 patients, epilepsy was the reason for a standard MRI of the brain; in another patient with myasthenia, MRI was performed as part of a comprehensive examination. All patients, in addition to standard MRI, underwent a modification of the real-time scanning protocol to include contrast, tractography (DTI), and perfusion techniques (ASL/DSC). In 1 case, with questionable results, the results of a modification of the standard MRI protocol, high-resolution MRI (HR MRI) and hybrid positron emission CT with 11C-methionine (PET/CT with 11C-MET) were combined. RESULTS: Seven patients underwent epileptic surgery and 1 patient was operated on for a tumor. In 4 out of 8 patients, based on the results of a modification of the standard MRI protocol, radiological signs of a neoplastic process were identified, which suggested a low-grade tumor. One of them needed PET/CT to confirm the assumption. The results of pathomorphological examination correlated with the direct diagnosis for surgical treatment. One of the 4 patients was suspected to have dysembryoplastic neuroepithelial tumor (DNET) based on the results of the protocol modification, which was also confirmed by pathological examination. In another 4 patients in whom it was possible to narrow the differential between FCD type II and DNET based on the results of the modification, FCD IIb was pathomorphologically verified. CONCLUSION: The proposed modification of the standard MRI protocol can significantly facilitate the differential diagnosis between the neoplastic and dysplastic origin of an epileptogenic substrate of unknown etiology, which in turn affects the patient's management tactics.

Epilepsy detection based on multi-head self-attention mechanism.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization.

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.

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. METHOD: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. CONCLUSION: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

Multiscale distribution entropy analysis of short epileptic EEG signals.

This paper proposes an information-theoretic measure for discriminating epileptic patterns in short-term electroencephalogram (EEG) recordings. Considering nonlinearity and nonstationarity in EEG signals, quantifying complexity has been preferred. To decipher abnormal epileptic EEGs, i.e., ictal and interictal EEGs, via short-term EEG recordings, a distribution entropy (DE) is used, motivated by its robustness on the signal length. In addition, to reflect the dynamic complexity inherent in EEGs, a multiscale entropy analysis is incorporated. Here, two multiscale distribution entropy (MDE) methods using the coarse-graining and moving-average procedures are presented. Using two popular epileptic EEG datasets, i.e., the Bonn and the Bern-Barcelona datasets, the performance of the proposed MDEs is verified. Experimental results show that the proposed MDEs are robust to the length of EEGs, thus reflecting complexity over multiple time scales. In addition, the proposed MDEs are consistent irrespective of the selection of short-term EEGs from the entire EEG recording. By evaluating the Man-Whitney U test and classification performance, the proposed MDEs can better discriminate epileptic EEGs than the existing methods. Moreover, the proposed MDE with the moving-average procedure performs marginally better than one with the coarse-graining. The experimental results suggest that the proposed MDEs are applicable to practical seizure detection applications.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction.

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.

Genetic heterogeneity in epilepsy and comorbidities: insights from Pakistani families.

BACKGROUND: Epilepsy, a challenging neurological condition, is often present with comorbidities that significantly impact diagnosis and management. In the Pakistani population, where financial limitations and geographical challenges hinder access to advanced diagnostic methods, understanding the genetic underpinnings of epilepsy and its associated conditions becomes crucial. METHODS: This study investigated four distinct Pakistani families, each presenting with epilepsy and a spectrum of comorbidities, using a combination of whole exome sequencing (WES) and Sanger sequencing. The epileptic patients were prescribed multiple antiseizure medications (ASMs), yet their seizures persist, indicating the challenging nature of ASM-resistant epilepsy. RESULTS: Identified genetic variants contributed to a diverse range of clinical phenotypes. In the family 1, which presented with epilepsy, developmental delay (DD), sleep disturbance, and aggressive behavior, a homozygous splice site variant, c.1339-6 C > T, in the COL18A1 gene was detected. The family 2 exhibited epilepsy, intellectual disability (ID), DD, and anxiety phenotypes, a homozygous missense variant, c.344T > A (p. Val115Glu), in the UFSP2 gene was identified. In family 3, which displayed epilepsy, ataxia, ID, DD, and speech impediment, a novel homozygous frameshift variant, c.1926_1941del (p. Tyr643MetfsX2), in the ZFYVE26 gene was found. Lastly, family 4 was presented with epilepsy, ID, DD, deafness, drooling, speech impediment, hypotonia, and a weak cry. A homozygous missense variant, c.1208 C > A (p. Ala403Glu), in the ATP13A2 gene was identified. CONCLUSION: This study highlights the genetic heterogeneity in ASM-resistant epilepsy and comorbidities among Pakistani families, emphasizing the importance of genotype-phenotype correlation and the necessity for expanded genetic testing in complex clinical cases.

The evaluation of the training, "the approach to epilepsy and epileptic seizure," which was given to teachers in Türkiye.

INTRODUCTION: Epilepsy is the most common neurological disorder among humans after headaches. According to the World Health Organization, approximately 50-65 million individuals were diagnosed with epilepsy throughout the world, and around two million new cases of epilepsy are added to this figure every year. METHODS: Designed as descriptive and cross-sectional research, this study was performed on 132 elementary school teachers. Training on epilepsy and epileptic seizure was given to teachers. The pretest and posttest research data were collected with the face-to-face interview method. In this process, the epilepsy knowledge scale was used as well as a survey form that had questions designed to find out about teachers' personal characteristics. The Statistical Package for Social Science 25.0 was utilized in the statistical analysis of research data. In the research, the statistical significance was identified if the p-value was below.05 (p < .05). RESULTS: Of all teachers participating in the study, 59.1% were female, 90.2% were married, and 47.7% witnessed an epilepsy seizure before. The mean of teachers' pretest epilepsy knowledge scores was 8.43 ± 4.31 points before the training while the mean of their posttest epilepsy knowledge scores was 12.65 ± 2.48 points after the training. The difference between the means of pretest and posttest scores was statistically significant (p = .000). After the training, there was a statistically significant increase in means of scores obtained by teachers from each item of the epilepsy knowledge scale (p < .05). CONCLUSIONS: As there was a statistically significant improvement in levels of teachers' knowledge about both epilepsy and epileptic seizure after the training, it is recommended that the training about the approach to epilepsy and epileptic seizure be given to all teachers, and additionally, including these topics in the course curricula of universities is recommended.

PYRIDOXINE-dependent epilepsy (PDE): An observational study of neonatal cases on the role of pyridoxine in patients treated with standard anti-seizure medications.

BACKGROUND: The main objective of this study was to evaluate the neurological consequences of delayed pyridoxine administration in patients diagnosed with Pyridoxin Dependent Epilepsies (PDE). MATERIALS AND METHODS: We reviewed 29 articles, comprising 52 genetically diagnosed PDE cases, ensuring data homogeneity. Three additional cases were included from the General Pediatric Operative Unit of San Marco Hospital. Data collection considered factors like age at the first seizure's onset, EEG reports, genetic analyses, and more. Based on the response to first-line antiseizure medications, patients were categorized into four distinct groups. Follow-up evaluations employed various scales to ascertain neurological, cognitive, and psychomotor developments. RESULTS: Our study includes 55 patients (28 males and 27 females), among whom 15 were excluded for the lack of follow-up data. 21 patients were categorized as "Responder with Relapse", 11 as "Resistant", 6 as "Pyridoxine First Approach", and 2 as "Responders". The neurological outcome revealed 37,5 % with no neurological effects, 37,5 % showed complications in two developmental areas, 15 % in one, and 10 % in all areas. The statistical analysis highlighted a positive correlation between the time elapsed from the administration of pyridoxine after the first seizure and worse neurological outcomes. On the other hand, a significant association was found between an extended latency period (that is, the time that elapsed between the onset of the first seizure and its recurrence) and worse neurological outcomes in patients who received an unfavorable score on the neurological evaluation noted in a subsequent follow-up. CONCLUSIONS: The study highlights the importance of early recognition and intervention in PDE. Existing medical protocols frequently overlook the timely diagnosis of PDE. Immediate administration of pyridoxine, guided by a swift diagnosis in the presence of typical symptoms, might improve long-term neurological outcomes, and further studies should evaluate the outcome of PDE neonates promptly treated with Pyridoxine.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Approach to Patients with Seizures and Epilepsy: A Guide for Primary Care Physicians.

Seizures and epilepsy are common neurologic conditions that are frequently encountered in the outpatient primary care setting. An accurate diagnosis relies on a thorough clinical history and evaluation. Understanding seizure semiology and classification is crucial in conducting the initial assessment. Knowledge of common seizure triggers and provoking factors can further guide diagnostic testing and initial management. The pharmacodynamic characteristics and side effect profiles of anti-seizure medications are important considerations when deciding treatment and counseling patients, particularly those with comorbidities and in special populations such as patient of childbearing potential.

Long-term seizure diary tracking habits in clinical studies: Evidence from the Human Epilepsy Project.

OBJECTIVE: To characterize seizure tracking patterns of people with focal epilepsy using electronic seizure diary entries, and to assess for risk factors associated with poor tracking. METHODS: We analyzed electronic seizure diary data from 410 participants with newly diagnosed focal epilepsy in the Human Epilepsy Project 1 (HEP1). Each participant was expected to record data each day during the study, regardless of seizure occurrence. The primary outcome of this post-hoc analysis was whether each participant properly tracked a seizure diary entry each day during their study participation. Using finite mixture modeling, we grouped patient tracking trajectories into data-driven clusters. Once defined, we used multinomial modeling to test for independent risk factors of tracking group membership. RESULTS: Using over up to three years of daily seizure diary data per subject, we found four distinct seizure tracking groups: consistent, frequent at study onset, occasional, and rare. Participants in the consistent tracking group tracked a median of 92% (interquartile range, IQR: 82%, 99%) of expected days, compared to 47% (IQR:34%, 60%) in the frequent at study onset group, 37% (IQR: 26%, 49%) in the occasional group, and 9% (IQR: 3%, 15%) in the rare group. In multivariable analysis, consistent trackers had lower rates of seizure days per tracked year during their study participation, compared to other groups. SIGNIFICANCE: Future efforts need to focus on improving seizure diary tracking adherence to improve quality of outcome data, particularly in those with higher seizure burden. In addition, accounting for missing data when using seizure diary data as a primary outcome is important in research trials. If not properly accounted for, total seizure burden may be underestimated and biased, skewing results of clinical trials.

A quality of care assessment for women of childbearing potential with epilepsy in Bhutan: An observational study.

BACKGROUND & OBJECTIVE: In lower-middle income countries such as Bhutan, the treatment gap for epilepsy is over 50% as compared to a treatment gap of less than 10% in high-income countries. We aim to analyze the quality of epilepsy care for women of childbearing potential in Bhutan using the Quality Indicators in Epilepsy Treatment (QUIET) tool, and to assess the usefulness of the tool's section for women with active epilepsy (WWE) in the Bhutanese setting. METHODS: A prospective convenience cohort was enrolled in Thimphu, Paro, Punakha, and Wangdue, Kingdom of Bhutan, in 2022. Bhutanese women of childbearing potential at the time of enrollment (18-44 years old) were evaluated for the diagnosis of active epilepsy and underwent a structured survey-based interview with Bhutanese staff. Participants were surveyed on their epilepsy, pregnancy, and antiseizure medicine (ASM) histories. The clinical history and quality of epilepsy care of adult WWE were assessed using a section of the QUIET tool for women, an instrument originally developed by the U.S. Department of Veterans Affairs to analyze the quality of epilepsy care for American adults. RESULTS: There were 82 Bhutanese WWE of childbearing potential, with mean age of 30.6 years at enrollment (range 18-44, standard deviation (SD) 6.6) and mean age of 20.3 years at epilepsy diagnosis (range 3-40, SD 8.0)). 39 % (n = 32) had a high school or above level of education, and 42 % (n = 34) were employed. 35 % (n = 29) reported a seizure within the prior week, and 88 % (n = 72) reported a seizure within the prior year. 49 % (n = 40) of participants experienced > 100 lifetime seizures. All but one participant took antiseizure medications (ASMs). At enrollment, participants presently took no (n = 1), one (n = 3), two (n = 37), three (n = 25), four (n = 11), or over five (n = 5) ASMs. The most common ASMs taken were levetiracetam (n = 40), phenytoin (n = 27), carbamazepine (n = 23), phenobarbital (n = 22), and sodium valproate (n = 20). 61 % of all WWE took folic acid. Of the 40 previously pregnant WWE, eight (20 %) took folic acid during any time of their pregnancy. 35 % (n = 29) used betel nut (doma, quid) and 53 % (n = 21) of pregnant WWE used betel nut during pregnancy. CONCLUSIONS: Based on data about WWE participants' ASM, supplement, and substance use, our study identified the high use of first generation ASMs (including valproate), frequently in polytherapy, and betel nut use as treatment gaps in women of childbearing potential age with active epilepsy in Bhutan. To address these gaps for locations such as Bhutan, we propose modifications to the QUIET tool's "Chronic Epilepsy Care for Women" section.