Seizures

Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.

UNLABELLED: High-channel-density (HCD) electroencephalography (EEG) enables fine-grained neural sensing but is constrained by high hardware costs, spatial complexity, and limited portability. This study developed a deep learning-based method to reconstruct high-density EEG signals from low-channel-density (LCD) inputs, enabling more practical and affordable brain-monitoring systems. This study int...

Cost-effectiveness analysis of resective epilepsy surgery in drug-resistant patients: an artificial intelligence data modeling.

BACKGROUND: Resective epilepsy surgery has been proven to reduce the number of seizures and improve quality of life in patients with drug-resistant epilepsy (DRE) but implies high direct costs. Cost-effectiveness analyses have shown that surgery is cost-effective. We aimed to evaluate whether we can determine the cost-effectiveness of surgery for DRE using artificial intelligence-based extraction ...

Development of a deep learning tool to detect drug-resistance epilepsy with EEG.

INTRODUCTION: Drug-resistant epilepsy affects about 30% of patients and is linked to poorer outcomes. Deep learning can extract complex patterns from ...

Single-channel EEG-based seizure prediction using deep learning.

Reliable seizure prediction can improve patient safety by enabling timely protective actions, yet most high-performing approaches depend on multichann...

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...

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 a...

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...

EEG-AI: An agentic system for AI-assisted semi-automated EEG preprocessing and artifact removal.

BACKGROUND: EEG is widely used to identify neural markers, personalize treatments, and evaluate interventions. However, low signal-to-noise ratio and ...

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 ...