Seizures

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 mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and responsive neurostimulation offer an important and established ...

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)-video signals for automatic epileptic spasms (ES) detection. METHODS: This retrospective cohort study with internal cross-validation and multicenter external validation was conducted from July 2022 to May 2025. It included 252 patients with ES from...

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...

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.

Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition affecting mood, anxiety, learning, and sleep. Electroencephalogram (...

Prediction of depressive episodes based on clinical features, cognitive characteristics, inflammation-related proteins, and EEG data.

The absence of clinically validated biomarkers and objective diagnostic protocols hinders the accurate and effective diagnosis of depression. Although...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...

Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder.

BACKGROUND: Major Depression (MDD) is a potentially life-threatening condition that ranks among the diseases with the highest global burden. Despite i...