Neurology

Seizures

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

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Plasma proteomics of seizure-associated changes in epilepsy

Fluid biomarkers are emerging as crucial markers for diagnosis and disease monitoring in neurology. ...

Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study

Objective: Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Res...

Synthesising Interictal Epileptiform Discharges With Generative Adversarial Network

Interictal epileptiform discharges (IEDs) are reliable biomarkers in electroencephalograms for epile...

A Deep Lightweight Convolutional Neural Network for Detecting Artifacts in Continuous EEG Signals

This study aimed to develop and validate a system of specialized deep lightweight convolutional neur...

Real-Time EEG-Based Epileptic Seizure Prediction Using Artificial Intelligence: A Systematic Review

Epilepsy affects approximately 50 million people worldwide, and seizures remain difficult to predict...

Epileptic Seizure Detection based on Different Events with XAI and Early Aid System for Patient Aid

The goal of this study is seizure detection in four class datasets for different seizure stages in e...

Automated Seizure Classification Using Multimodal Large Language Models

Accurately distinguishing between epileptic seizures (ES) and nonepileptic seizures (NES) is a signi...

Alzheimer’s Disease Stage Classification via Multimodal CNN on EEG Spectrograms and Cube-Drawing Images

Clinicians currently lack reliable tools to determine, at the point of mild cognitive impairment (MC...

Towards Automated Neonatal EEG Analysis: Multi-Center Validation of a Reliable Deep Learning Pipeline

To evaluate the reliability and generalization of NeoNaid, a fully automated software tool for neona...

Interpretable Machine Learning for Epileptic Seizure Detection on the BEED Using LIME with an Ensemble Network

This study aims to identify seizures in four different stages among epileptic patients, utilizing th...

The Seizure Embedding Map: A Spatio-Temporal Transformer for Comparing Patients by Ictal Intracranial EEG Features at Scale

Planning invasive treatment for medication-resistant epilepsy relies on qualitatively interpreting s...

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of pati...

Aging Detection Based on Dynamic State Transitions in Instantaneous Hilbert-Based Spatio-Temporal EEG Features

The spatial distribution of electroencephalography (EEG) oscillatory power and its temporal transiti...

Mobility Function and Aperiodic Electrocortical Activity in Younger and Older Adults

Mobility declines with age to the extent that walking speed is often considered a vital sign. Identi...

EEG-Based Prediction of rTMS Treatment Response in Depression: Nonlinear Features and Machine Learning with Minimal Electrode

Repetitive transcranial magnetic stimulation (rTMS) is an established intervention for treatment-res...

Topological Entropy and Homology Reveal Interpretable and Real-Time Neural Signatures in Pediatric EEG

Decoding neural states from pediatric EEG in naturalistic settings remains challenging due to signal...

Quantitative EEG-Based Deep Learning for Neonatal Seizure Detection using Conv-LSTM

Neonatal seizures cause significant morbidity and mortality, both acutely and in the long term, cont...

Bedtime Brain State Predicts the Impact of Closed-Loop Auditory Stimulation on Sleep and Cognition

Sleep interventions targeting slow-wave activity (SWA) show heterogeneous effects across individuals...

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