Seizures

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is widely used for diagnosing sleep disorders, its potential to assess future health risks has not been fully explored. This study aimed to develop and evaluate an interpretable framework to identify physiological patterns in PSG data linked to cardiovascul...

Enhanced cortical connectivity during passive robotic-assisted gait training in individuals with spinal cord injury: An EEG study.

BACKGROUND: Robotic-assisted gait training (RAGT) has emerged as a promising strategy to promote neuroplasticity and motor recovery in individuals with spinal cord injury (SCI). This study investigates cortical connectivity during passive robotic gait compared with standing, hypothesizing greater sensor-level network connectivity during gait than during standing. METHODS: Twenty-three individuals ...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain.

Repetitive transcranial magnetic stimulation (rTMS) to the primary motor cortex (M1) provides significant pain relief in ∼45% of chronic pain patients...

Bidirectional Mamba-2 Boosts EEG Super-Resolution via Regression and Diffusion.

MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond ...

A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG.

Postoperative delirium (POD) poses a significant risk to patients, and accurate prediction of postoperative delirium can provide guidance for pos...

E2T: EEG-to-Trajectory Transformer for Motor Imagery-based Fully-DoF Motion Prediction.

Brain-computer interfaces (BCIs) using electroen-cephalography (EEG) enable non-invasive, real-time interaction for individuals with motor impairments...

MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm.

Mental health disorders like depression and anxiety pose global challenges, requiring accurate, non-invasive detection methods. Classical modes of dia...

Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.

Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this st...

Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.

INTRODUCTION: To evaluate the progress of artificial intelligence (AI)-based tools in interpreting clinical data, to compare the existing models, to i...

Severity-dependent alterations of EEG microstate dynamics in obsessive-compulsive disorder.

BACKGROUND: While elevated symptom severity in obsessive-compulsive disorder (OCD) is associated with a profound clinical burden and escalating psychi...

Consciousness Monitoring and Outcome Prediction Using EEG Connectivity in Severe Traumatic Brain Injury.

Neurological prognostication of patients in post-traumatic coma remains challenging due to the paucity of reliable markers in the acute phase. We aime...

Patient-specific instantaneous spatial temperature maps for MR-guided laser interstitial thermal therapy using a physics-assisted deep learning framework.

PURPOSE: Accurate prediction of the laser energy absorption and corresponding thermal spread is essential for safe and effective outcomes in magnetic ...

PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations.

Physiological artifacts pose persistent challenges in electroencephalogram (EEG) data acquisition, often compromising interpretation and post-analysis...

A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.

OBJECTIVE: Currently available wearable devices for detecting focal seizures primarily target major motor seizures or involve semi-invasive subscalp i...

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep State Classification.

OBJECTIVE: Fetal sleep state classification is essential for identifying neurodevelopmental complications like hypoxia, but manual annotation is subje...

Diagnostic performance of AI-based EEG interpretation versus human clinical experts for epilepsy detection: systematic review and meta-analysis.

BACKGROUND: Electroencephalography (EEG) interpretation for epilepsy diagnosis faces persistent challenges including specialist shortages, variable in...

Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.

UNLABELLED: High-channel-density (HCD) electroencephalography (EEG) enables fine-grained neural sensing but is constrained by high hardware costs, spa...

Cost-effectiveness analysis of resective epilepsy surgery in drug-resistant patients: an artificial intelligence data modeling.

BACKGROUND: Resective epilepsy surgery has been proven to reduce the number of seizures and improve quality of life in patients with drug-resistant ep...

Development of a deep learning tool to detect drug-resistance epilepsy with EEG.

INTRODUCTION: Drug-resistant epilepsy affects about 30% of patients and is linked to poorer outcomes. Deep learning can extract complex patterns from ...

Single-channel EEG-based seizure prediction using deep learning.

Reliable seizure prediction can improve patient safety by enabling timely protective actions, yet most high-performing approaches depend on multichann...