Seizures

Status, challenges, and future directions of machine learning in the management of epilepsy: a systematic review and meta-analysis.

BACKGROUND: Despite advances in epilepsy treatment options, selecting the appropriate therapy for an individual with epilepsy is a process of trial and error. Machine learning holds the potential to support clinical decision making. We aimed to provide an overview of the role of machine learning in epilepsy management and discuss future directions. METHODS: In this systematic review and meta-analy...

A novel approach to dyslexia detection: combining EEG data and cognitive tasks with advanced machine learning.

Existing dyslexia detection methods typically rely on either EEG or screening tests. This study introduces a multimodal two-stage methodology for dyslexia detection that integrates both EEG signals and screening test data, using spectral features (Shannon entropy and Power Spectral Density) across various frequency bands. The novelty of the proposed approach stems in collecting firsthand paired EE...

Machine learning classification of mild Alzheimer's disease using EEG during emotional processing.

BackgroundMild Alzheimer's disease (AD) is associated with alterations in brain activity, which can be detected using electroencephalography (EEG). In...

Harmonizing complexity and efficiency: Helix Fusion HarmonyNet's breakthrough in EEG-based postoperative delirium recognition.

Postoperative delirium (POD) is a common perioperative complication involving central nervous system dysfunction, particularly among critically ill an...

Clinically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies.

INTRODUCTION: Brain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression o...

Modern generative large language models in epilepsy care: a scoping review of current applications, challenges, and future directions.

Modern generative large language models (LLMs) are increasingly being evaluated in epilepsy-related clinical tasks, but the evidence remains fragmente...

Stochastic Sparse Sampling: A Variable-Length Time Series Classification Framework for Seizure Onset Zone Localization.

Variable-length time series classification (VTSC) problems are prevalent in healthcare applications, such as heart rate monitoring and electrophysiolo...

Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.

BACKGROUND: Artificial intelligence (AI) is increasingly explored in veterinary neurology for pattern recognition, prediction and clinical decision su...

Unsupervised Ensemble Learning for Active Drug-Induced Liver Injury Surveillance: Integrating Pharmacokinetic Burden With Enzyme Trajectories.

PURPOSE: Traditional drug-induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. ...

Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening.

Attention-deficit/hyperactivity disorder (ADHD) affects millions globally, yet current diagnostic approaches rely on subjective behavioral assessments...

Using large language models to investigate patients' and caregivers' perceptions on SUDEP: A case study with an online epilepsy population.

BACKGROUND: Sudden Unexpected Death in Epilepsy (SUDEP) is a leading cause of epilepsy-related mortality, yet remains under-communicated in clinical p...

Patient-independent seizure onset zone localization with generalizable feature learning and multi-task supervision.

Most existing ictal stereoelectroencephalography (SEEG)-based seizure onset zone (SOZ) localization methods rely on patient-specific training, limitin...

Shared texture-like representations underlie deep neural network alignment with human visual processing.

Deep neural networks (DNNs) excel at predicting neural responses across the visual hierarchy,1,2,3,4,5 a success widely interpreted as evidence of sha...

The design and optimization of a chitosan-based lamotrigine-loaded intranasal mucoadhesive nanomicelle solution using response surface methodology and artificial neural networks.

The study aimed to develop and optimize chitosan-based mucoadhesive nanomicelles for intranasal delivery of lamotrigine (LTG), to enhance epilepsy tre...

TensorPat: Explainable artificial intelligence for EEG-based violence-stimulus classification.

Background: EEG responses to violence-related visual stimuli are relevant to neuroscience and digital forensics. Yet most EEG classification models em...

When multimodal fusion helps: An ablation study of EEG-ECG fusion strategies for emotion recognition.

Multimodal physiological signal fusion-particularly electroencephalography (EEG) and electrocardiography (ECG)-is widely assumed to improve emotion re...

Machine-based classification of epileptiform activity in selected EEG excerpts from genetic generalised epilepsy.

OBJECTIVES: To evaluate whether the deep learning model IGENet-TS, a time-domain convolutional neural network (CNN), can classify expert-selected EEG ...

Personalized ECG-based artificial intelligence models for early seizure prediction.

Seizure prediction is critically important, as it can help prevent serious injuries, improve quality of life, and potentially reduce the risk of SUDEP...

MTGNet: A task-oriented and spectrally guided framework for EEG denoising.

Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs), but its microvolt-level signals are easily contaminated by electromyo...

[A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].

Accurate neurological outcome assessment after cardiac arrest is critical for clinical diagnosis and treatment. Existing electroencephalogram (EEG) pr...