Seizures

Insular morphological abnormalities in focal to bilateral tonic-clonic seizures.

PURPOSE: Focal to bilateral tonic-clonic seizures (FBTCS) is the most severe form of epileptic seizures, posing a major challenge in both management and research. This study aimed to characterize microstructural abnormalities in the normal-appearing cortex of patients with FBTCS and assess their potential for individual-level identification. METHODS: We retrospectively included 135 unilateral drug...

Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes.

This study proposes a fuzzy machine learning framework for optimizing antiepileptic drug selection using Quantitative Structure-Property Relationship (QSPR) modeling under pharmacological uncertainty. Feature relevance was assessed using Random Forest-based importance and SelectKBest with mutual information, and a feedforward neural network was trained with 5-fold cross-validation. Fuzzy membershi...

Closed-loop stimulation modulates attention shifting in children.

Spontaneous fluctuations in attention can impede adaptation to changing goals and environments. Endogenous control over attentional shifts, referred t...

Exploring human GABA transporter 3 binders for epilepsy through quantum reactivity analysis, molecular dynamics, machine learning prediction, and network pharmacology.

CONTEXT: Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to convention...

MPAN: A Multi-Prototype Adaptive Network for Few-Shot EEG-Based Biometric Recognition.

Biometric recognition based on electroencephalography (EEG), which captures intrinsic neural dynamics via scalp-recorded electrical activity, has show...

NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

To address the inherent complexity and nonlinearity of electroencephalogram (EEG) signals, this study proposes a refined classification framework, Neu...

Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.

This study introduces a novel adaptive deep learning framework for EEG-based schizophrenia diagnosis that addresses the limitations of existing static...

DeepEpiX: a software for visualization, annotation and automatic epileptical spike detection in MEG recordings.

BACKGROUND: Magnetoencephalography (MEG) non-invasively records brain activity. It is widely used in presurgical evaluation of drug-resistant epilepsy...

Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: Recent advances, current challenges, and future directions.

Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental ...

Noninvasive neurophysiological diagnostics of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a scoping review.

BACKGROUND: Delayed cerebral ischemia (DCI) is a major complication following aneurysmal subarachnoid hemorrhage (aSAH), affecting outcomes. Given its...

A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG.

Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer's disease. Electroe...

A multi-view TSK fuzzy system with deformable Gaussian membership functions and rule-level attention for classification.

This study presents a novel multi-view TSK fuzzy system that integrates deformable Gaussian membership functions with a rule-level attention mechanism...

Determinants and Machine Learning Prediction of Subtherapeutic Sodium Valproate Concentrations in Epilepsy Management in Xinjiang, China.

BACKGROUND AND OBJECTIVE: Valproic acid is a classic antiepileptic drug; however, it is characterized by a narrow therapeutic window, limited safety m...

Brain-age defines a structural signature of outcomes after vagus nerve stimulation in children.

INTRODUCTION: Outcomes following vagus nerve stimulation (VNS) are difficult to predict prior to surgery in pediatric drug-resistant epilepsy (DRE). W...

AI-Driven Mapping of Seizure Spread Patterns.

OBJECTIVE: The focus of epilepsy research has largely been on seizure onset; however, physicians typically examine the patterns of seizure spread past...

Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAA receptors and GABA transporter 1.

This review examines how recent genetic and technological advances have transformed our understanding and treatment of genetic epilepsies (GEs), with ...

Cerebro Wave Bee transformer to leverage EEG based hand movement classification using optimized bio-inspired learning rates.

Electroencephalogram (EEG) based classification of hand movements have an eloquent significance in the diverse fields like biomedical engineering, neu...

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals.

Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitig...

A multifeature machine learning and resting-state EEG study reveals differences in beta oscillation in late-life depression with or without mild cognitive impairment.

BACKGROUND: Late-life depression (LLD) often co-occurs with mild cognitive impairment (MCI), and patients with LLD and MCI (LLD-MCI) have an increased...

Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.

BACKGROUND: Depression is one of the most prevalent mental disorders globally, severely affecting individuals' emotional, cognitive, and physical func...