Seizures

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

Interictal epileptiform discharges (IEDs) are essential for epilepsy diagnosis, yet visual electroencephalogram (EEG) analysis remains subjective and laborious. To address this, we developed Epilepsy-IEDs, an automated machine learning model for IED detection. Trained on 141 scalp EEG recordings (2,597 IEDs and 4,633 non-IEDs), the model was evaluated using four algorithms, with a separate daytime...

Cross-Level Topological Framework: Learning Explainable Region-Channel Representations from EEG Signals for Emotional Decoding.

Constructing functional connectivity networks from electroencephalogram (EEG) channels and using graph neural networks for emotion recognition have emerged as a significant technical route in EEG emotion recognition. However, most existing approaches are limited to estimating brain graph networks based on EEG full-channel signals, failing to adequately explore the representations between and withi...

Diagnostic yield of electroencephalographyin the emergency department: protocol for the EMINENCE-M multicentre retrospective observational study.

INTRODUCTION: Emergency EEG (emEEG) is increasingly used in the emergency department (ED), but its diagnostic yield remains uncertain. This protocol d...

Evaluating multi-level membership inference risk in federated EEG learning.

Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communi...

Precision neuroprotection in cardiac surgery: a structured narrative review of multimodal, data-driven perioperative care.

Neurological injury remains a major contributor to morbidity, mortality, and long-term cognitive decline in patients undergoing cardiac surgery, despi...

Diagnosis of ADHD in children from EEG signals using amplitude modulation features.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and accurate diagnosis of this disorde...

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalograp...

Health-Related Quality of Life and Family Impact Among Children with Epilepsy: The Role of Seizure Control.

INTRODUCTION: Seizure control is the primary therapeutic goal in pediatric epilepsy, yet its multidimensional impact on health-related quality of life...

The AI dispatcher copilot: beyond cardiac arrest recognition to dynamic large language model-assisted Tele-CPR.

AIM: Out-of-hospital cardiac arrest (OHCA) remains a leading cause of death. Although emergency medical dispatchers represent the first link in the Ch...

Decoding concealed information using multimodal neurophysiological signals.

Detecting concealed information is a critical challenge in forensic investigations, security screening, and cognitive neuroscience. Conventional appro...

A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.

Depression disorder (DD) is a common mental disorder and a leading contributor to the global burden of disease. However, accurate diagnosis remains ch...

A novel transformer architecture for EEG decoding and neuroscientific analysis.

Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limitin...

Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.

The quick and accurate diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) is a significant and unresolved challenge in clinical n...

Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets.

Antiepileptic drugs (AEDs) were frequently employed in glioma patients, especially those with low-grade glioma (LGG), in which epilepsy manifested in ...

A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.

PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signa...

Variational autoencoder for interpretable seizure onset phases detection.

OBJECTIVE: In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patie...

Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.

Automated seizure detection from long-term scalp electroencephalography (EEG) remains challenging because seizure windows are sparse, channel configur...

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

Parkinson's disease (PD), a prototypical neurodegenerative disorder, poses significant challenges for early diagnosis. Motivated by recent advances in...

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

The classification of electroencephalogram (EEG) signals plays an important role in neuroscience research and clinical diagnosis of epileptic seizures...

Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing.

BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical popula...