Seizures

Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy being investigated for established status epilepticus. Early drug exposure estimates could inform dosing, but intensive pharmacokinetic sampling is challenging in pediatric emergency settings due to logistical and ethical constraints. Existing popul...

Trends in the studies of pharmacoresistant epilepsy- a review based on literature analysis (2015-2025).

Drug-resistant epilepsy (DRE) is a complex neurological disease that accounts for 30%-40% of all epilepsy cases. Its pathogenesis and treatment have always been research hotspots in this field. In recent years, interdisciplinary research on DRE has gradually become a hot topic owing to the research and application of gene sequencing, neuroimaging, new antiepileptic therapies, and artificial intell...

Transcriptional signatures and topological reorganization of morphometric similarity networks in temporal lobe epilepsy with unilateral hippocampal sclerosis.

OBJECTIVE: To delineate morphometric similarity network (MSN) topological abnormalities and their underlying spatial transcriptomics in the normal-app...

Physiologically inspired modeling of cortical dynamics through spiking neural networks.

the characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several metho...

Physics-informed graph neural networks for robust cross-patient epileptic seizure prediction via chimera state detection.

BACKGROUND: Epilepsy affects approximately 50 million individuals worldwide, with 30% experiencing drug-resistant seizures despite optimal pharmacolog...

Clinical applications of EEG connectivity in acute brain injuries: A systematic review.

OBJECTIVE: While connectivity methods have been widely studied as predictors of recovery in chronic disorders of consciousness (DoC), evidence for EEG...

Machine learning in epilepsy.

Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excit...

AI-Driven Multimodal Analysis of Neuroimaging and Speech Data for Diagnosis of Alzheimer's, Parkinson's, and Epilepsy.

This study investigates the application of machine learning (ML) techniques combined with neuroimaging and speech signal processing for the early dete...

A scalable EEG-based spatial neglect detection system in augmented reality for stroke patients.

BACKGROUND: Spatial neglect is a common visuospatial attention disorder following a stroke. To overcome weaknesses associated with classic pen-and-pap...

Self-Supervised Learning With Adaptive Graph Modeling for EEG-Based Epileptic Seizure Classification.

OBJECTIVE: Epileptic seizure classification using EEG signals remains a significant challenge due to complex spatial-temporal dependencies, limited la...

Energy-Efficient EEG-Based Autism Spectrum Disorder Detection Using a Hyperbolic Attention Neural Network.

Long-term physiological monitoring using wearable wireless systems represents a paradigm change in next-generation e-health applications. Specifically...

Channel Transformer-Based Generative Adversarial Network With Multi-Instance Attention and Nutcracker Optimization for Automatic Seizure Detection Using EEG.

The current literature on automatic seizure detection based on EEG has obtained significant accuracy, but most of them still have difficulties in proc...

Optimized Seizure Detection in EEG Using Dual-Branch Feature Fusion and Machine Learning Technique.

Epilepsy is a neurological disorder of the brain that generates seizures due to abnormal electrical activity. The diagnosis and management of the dise...

Scalp Electroencephalography Markers of Hidden Interictal Epileptiform Discharges in the Mesial Temporal Lobe.

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis but are often undetectable on scalp EEG (scEEG). This study aims to devel...

EEG Emotion Recognition With Uncertainty-Aware Contrastive Learning and Frequency-Aware Self-Attention.

Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for ...

A Review of Vagus Nerve Stimulation for Disease: Comprehensive Theory and Evidence for Mechanisms of Action.

Vagus nerve stimulation (VNS) is an established neuromodulatory therapy approved for epilepsy, depression, obesity, stroke rehabilitation, rheumatoid ...

Improved Hybrid Local Binary Structural Pattern Shallow Graph Deep Convolutional Attention Neural Networks With Synergistic Fibroblast Optimization for Automated Epileptic Seizure Detection and Diagnosis in EEG Signals.

Epileptic seizure (ES) detection from electroencephalography (EEG) signals is difficult because of noise and the intricate, patient-specific nature of...

Design and Development of an Automated Electroencephalogram Signal Processing and Diagnostic Support Architecture Employing Advanced Deep Learning Models for Early Accurate and Scalable Detection of Neurological Diseases.

BACKGROUND AND PURPOSE: Electroencephalogram (EEG) signals play a vital role in analyzing neurological activity and diagnosing various neurological di...

Artificial intelligence for adaptive neuromodulation in drug-resistant epilepsy.

Drug-resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortalit...

Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.

OBJECTIVE: This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG...