Seizures

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 arthritis, migraine, and cluster headaches. Its therapeutic potential has expanded dramatically, with growing evidence supporting its efficacy across a wide spectrum of neurological, psychiatric, cardiovascular, immunological, metabolic, and gastroin...

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 brain activity. Traditional methods often suffer from low accuracy, high computational costs, and poor generalization. To tackle these challenges, an improved hybrid local binary structural pattern shallow graph deep convolutional attention neural n...

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...

Feature alignment and enhancement network with guided tuning for non-stationary EEG classification.

Objective.Electroencephalogram (EEG) signal variability caused by external factors and subject differences limits the adaptation of motor imagery (MI)...

MuGEP: Multiplex Graph-Based Brain Network Modeling for Epileptic Seizure Prediction Using Intracranial EEG.

Accurate seizure prediction in advance is crucial for patients with epilepsy, as it helps prevent harm and improve life quality. Intracranial electroe...

Emotion recognition based on feature weight analysis of multiple physiological signals.

Emotion recognition stands as a complex and prominent challenge within contemporary artificial intelligence research. Deep learning on physiological s...

Neural feature alignment between large language models and brain activities: A knowledge-based framework for cross-modal analysis.

Recent studies examining the representational alignment between Large Language Models (LLMs) and brain activity show promising correspondences, yet la...

Altered degree centrality and resting-state functional connectivity in epilepsy patients with focal to bilateral tonic-clonic seizures.

Focal to bilateral tonic-clonic seizures (FBTCS) is a severe form of seizure associated with various adverse events. This study aimed to characterize ...

Machine learning-based prediction of postoperative delirium from intraoperative EEG signal alterations in brain functional connectivity.

OBJECTIVES: Postoperative delirium (POD) is a frequent complication following cardiovascular surgery and requires timely intervention. While early ris...

A hybrid deep learning framework for epileptic seizure prediction using scalp and intracranial EEG data.

Epilepsy is a prevalent neurological disorder affecting over 50 million people globally, often impairing quality of life due to unpredictable and recu...

Resting-State Electroencephalogram and Heart Rate Variability Jointly Predicts Intentional Episodic Memory Control Using Machine Learning Fusion.

Deficits in intentional control over episodic memory constitute a risk factor for multiple psychiatric disorders. Guided by a body-brain dynamic syste...

Explainable Convolutional Channel Ranking (ECCR) for EEG-Based Detection of Idiopathic Absence Seizures.

Accurate identification of EEG electrodes associated with epilepsy is essential for developing real-time diagnostic applications. This paper introduce...

Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide, affecting approximately 5%-7%...

3D ADHD-Net and DeepTrace: Decoding ADHD from EEG with neurophysiological insights.

EEG-based ADHD diagnosis models suffer from two persistent issues: data leakage and the lack of physiologically grounded interpretability, limiting cl...

Hippocampal-parietal directed connectivity mediates brain network reconfiguration between internal and external attention: An intracranial EEG study.

Attention is a cornerstone of cognitive function, and understanding its neural mechanisms is of great significance for both cognitive science and clin...

Nonlinear dynamical analysis of chaos, fractal complexity, and entropy in EEG signals during music therapy in ICU burn patients.

Music therapy (MT) is known to influence brain dynamics; however, its effects on nonlinear electroencephalogram (EEG) characteristics in clinical sett...

Personalized prediction of initial valproic acid dose in children with epilepsy using machine learning techniques.

INTRODUCTION: Valproic acid (VPA) is widely prescribed antiepileptic drug in children because of its broad-spectrum efficacy. However, marked inter-in...

EEG-RCPNet: a cross-subject cognitive impairment detection model based on limited-channel eeg signals.