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Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.

Epilepsy is one of the most common neurological disorders, yet its pathophysiology is poorly understood due to the high complexity of affected neuronal circuits. To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and non-epileptic subjects. We found that the largest transcriptomic changes occur in distinct neuronal subtypes from several families of principal neurons (L5-6_Fezf2 and L2-3_Cux2) and GABAergic interneurons (Sst and Pvalb), whereas other subtypes in the same families were less affected. Furthermore, the subtypes with the largest epilepsy-related transcriptomic changes may belong to the same circuit, since we observed coordinated transcriptomic shifts across these subtypes. Glutamate signaling exhibited one of the strongest dysregulations in epilepsy, highlighted by layer-wise transcriptional changes in multiple glutamate receptor genes and strong upregulation of genes coding for AMPA receptor auxiliary subunits. Overall, our data reveal a neuronal subtype-specific molecular phenotype of epilepsy. The pathophysiology of epilepsy is unclear. Here, the authors present single-nuclei transcriptomic profiling of human temporal lobe epilepsy from patients. They identified epilepsy-associated neuronal subtypes, and a panel of dysregulated genes, predicting neuronal circuits contributing to epilepsy.

CDKL5 deficiency disorder (CDD) was first identified as a cause of human disease in 2004. Although initially considered a variant of Rett syndrome, CDD is now recognised as an independent disorder and classified as a developmental epileptic encephalopathy. It is characterised by early-onset (generally within the first 2 months of life) seizures that are usually refractory to polypharmacy. Development is severely impaired in patients with CDD, with only a quarter of girls and a smaller proportion of boys achieving independent walking; however, there is clinical variability, which is probably genetically determined. Gastrointestinal, sleep, and musculoskeletal problems are common in CDD, as in other developmental epileptic encephalopathies, but the prevalence of cerebral visual impairment appears higher in CDD. Clinicians diagnosing infants with CDD need to be familiar with the complexities of this disorder to provide appropriate counselling to the patients' families. Despite some benefit from ketogenic diets and vagal nerve stimulation, there has been little evidence that conventional antiseizure medications or their combinations are helpful in CDD, but further treatment trials are finally underway.

Epilepsy has long been suspected to be governed by cyclic rhythms, with seizure rates rising and falling periodically over weeks, months, or even years. The very long scales of seizure patterns seem to defy natural explanation and have sometimes been attributed to hormonal cycles or environmental factors. This study aimed to quantify the strength and prevalence of seizure cycles at multiple temporal scales across a large cohort of people with epilepsy. This retrospective cohort study used the two most comprehensive databases of human seizures (SeizureTracker [USA] and NeuroVista [Melbourne, VIC, Australia]) and analytic techniques from circular statistics to analyse patients with epilepsy for the presence and frequency of multitemporal cycles of seizure activity. NeuroVista patients were selected on the basis of having intractable focal epilepsy; data from patients with at least 30 clinical seizures were used. SeizureTracker participants are self selected and data do not adhere to any specific criteria; we used patients with a minimum of 100 seizures. The presence of seizure cycles over multiple time scales was measured using the mean resultant length (R value). The Rayleigh test and Hodges-Ajne test were used to test for circular uniformity. Monte-Carlo simulations were used to confirm the results of the Rayleigh test for seizure phase. We used data from 12 people from the NeuroVista study (data recorded from June 10, 2010, to Aug 22, 2012) and 1118 patients from the SeizureTracker database (data recorded from Jan 1, 2007, to Oct 19, 2015). At least 891 (80%) of 1118 patients in the SeizureTracker cohort and 11 (92%) of 12 patients in the NeuroVista cohort showed circadian (24 h) modulation of their seizure rates. In the NeuroVista cohort, patient 8 had a significant cycle at precisely 1 week. Two others (patients 1 and 7) also had approximately 1-week cycles. Patients 1 and 4 had 2-week cycles. In the SeizureTracker cohort, between 77 (7%) and 233 (21%) of the 1118 patients showed strong circaseptan (weekly) rhythms, with a clear 7-day period. Between 151 (14%) and 247 (22%) patients had significant seizure cycles that were longer than 3 weeks. Seizure cycles were equally prevalent in men and women, and peak seizure rates were evenly distributed across all days of the week. Our results suggest that seizure cycles are robust, patient specific, and more widespread than previously understood. They align with the accepted consensus that most epilepsies have some diurnal influence. Variations in seizure rate have important clinical implications. Detection and tracking of seizure cycles on a patient-specific basis should be standard in epilepsy management practices. Australian National Health and Medical Research Council.

Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study.

Drug-resistant epilepsy (DRE) affects one-third of total epileptic patients and remains a major clinical challenge. Growing evidence implicates neuroinflammation as a key contributor to epileptogenesis and therapeutic resistance, but comprehensive, reproducible transcriptomic biomarkers are lacking. This study aimed to identify immune-inflammatory gene signatures associated with DRE using integrated transcriptomic profiling and machine-learning classifiers coupled with SHAP-based post-hoc explainability. Herein, this study curated and integrated 197 publicly available RNA-sequencing samples from cortical and hippocampal tissues across three Gene Expression Omnibus (GEO) datasets, comprising 162 epileptic and 35 non-epileptic control samples. After preprocessing and batch correction, differential expression analysis and ensemble-based feature selection were performed using the supervised classifiers Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ETs), and XGBoost. SHAP (SHapley Additive Explanations) values were used to prioritize features. External validation was conducted on an independent dataset ( n  = 68). Drug-gene interactions and molecular docking were applied to top-ranked genes. A set of 897 differentially expressed genes (DEGs), including 659 upregulated and 238 downregulated genes, was identified and was enriched for immune-inflammatory processes. Machine learning classifiers achieved high internal performance (mean ROC-AUC: 0.98–0.99) and robust external validation (ensemble ROC-AUC: 0.93). SHAP analysis consistently prioritized genes, including TNF, IL1B, and P2RY12. These features were biologically enriched in microglial and monocyte-related pathways. Drug-gene interaction identified multiple repurposable compounds, with Prasugrel and Pentamidine having strong binding affinities in docking studies. This study reveals reproducible immune-related transcriptomic biomarkers of drug-resistant epilepsy, highlights actionable targets for therapeutic repurposing, and provides a framework for precision medicine approaches in epilepsy. Code and processed data are available at: https://github.com/Tayyab-Ijaz/EpilepsyBiomarkerDrugs .

Epilepsy is a common brain disease that causes different types of seizures, with an incidence rate of nearly 1%. N7-methylguanosine (m7G) is a prevalent RNA modification that has attracted significant attention in recent research. In this study, we investigated the regulatory pattern and clinical significance of m7G methylation in epilepsy. Gene expression analysis of datasets GSE143272 and GSE190452 identified 8 differentially expressed m7G regulators (NUDT3, EIF4E3, LARP1, IFIT5, SNUPN, METTL1, EIF4A1, and LSM1) in epilepsy. Through consensus clustering, epilepsy patients were divided into two molecular subtypes based on m7G patterns. Enrichment and immune infiltration analyses revealed differences in immune cell infiltration and functions between the two subtypes, particularly in the levels of CD8 + T cells and cytolytic activity. Our findings also suggested that active m7G levels could promote oxidative phosphorylation in the neurons of epilepsy patients and decrease neuronal necroptosis activity. Machine learning algorithms were used to identify key m7G regulators (EIF4E3, NUDT3, SNUPN, LSM1, and METTL1), and a nomogram model was constructed based on these findings. Validation with serums and tissue samples from healthy controls and epilepsy patients confirmed the RNA expression levels of the identified m7G regulators. Overall, this study highlights the important role of m7G regulators in the immune microenvironment, cellular death, and oxidative phosphorylation in epilepsy patients. The insights gained from this research could potentially guide future therapy strategies for epilepsy patients and improve their outcomes.

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.

Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.