Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Personalizing Responsive Neurostimulation for Epilepsy.

Over the past 20 years, responsive neurostimulation (RNS), a closed-loop device for treating certain...

DynSeizureGAT: Multi-band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG.

The dynamic propagation of epileptic discharges complicates Drug-Resistant Epilepsy (DRE) seizure de...

A New Insight in Cellular and Molecular Signaling Regulation for Neural Differentiation Program.

Numerous neurological conditions impact the brain, spinal cord, and nerves, including neurodegenerat...

Super-resolution for localizing electrode grids as small, deformable objects during epilepsy surgery using augmented reality headsets.

PURPOSE: Epilepsy surgery is a potential curative treatment for people with focal epilepsy. Intraope...

Functional connectivity alterations of the pregenual anterior cingulate cortex by ketamine and the modulation by lamotrigine.

BACKGROUND: Neuroimaging studies have linked the beneficial effects of subanaesthetic ketamine doses...

Seminars in epileptology: How to diagnose status epilepticus in adults and children.

Status epilepticus (SE) can be regarded as the most severe expression of seizure activity characteri...

Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.

Low-beta (L, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, m...

Identifying and predicting EEG microstates with sequence-to-sequence deep learning models for online applications.

Electroencephalographic (EEG) microstates, as a non-invasive and high-temporal-resolution tool for a...

Early outcome-prediction with an automated EEG background trend in hypothermia-treated newborns with encephalopathy.

BACKGROUND: Therapeutic hypothermia is an intervention that improves outcomes and alters early outco...

TCANet: a temporal convolutional attention network for motor imagery EEG decoding.

Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of br...

Recent Advances in sMRI and Artificial Intelligence for Presurgical Planning in Focal Cortical Dysplasia: A Systematic Review.

BACKGROUND: Focal Cortical Dysplasia (FCD) is a leading cause of drug-resistant epilepsy, particular...

Fusion of FDG and FMZ PET Reduces False-Positives in Predicting Epileptogenic Zone.

BACKGROUND AND PURPOSE: Epilepsy, a globally prevalent neurologic disorder, necessitates precise ide...

POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG...

Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.

The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy effi...

Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders.

This study presents an artificial intelligence enhanced screening platform, DeepBAM, which enables ...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this ...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains ...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of...

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