Seizures

Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition affecting mood, anxiety, learning, and sleep. Electroencephalogram (EEG) signals assist diagnosis, but challenges include complexity, nonlinearity, non-stationarity, overlapping patterns, and limited feature interpretability. To address these issues, Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networ...

Prediction of depressive episodes based on clinical features, cognitive characteristics, inflammation-related proteins, and EEG data.

The absence of clinically validated biomarkers and objective diagnostic protocols hinders the accurate and effective diagnosis of depression. Although machine learning has been increasingly explored in psychiatric diagnosis, there remains a pressing need to develop a reliable tool that integrates multimodal data-such as clinical features, cognitive functions, electroencephalographic microstates, a...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...

Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder.

BACKGROUND: Major Depression (MDD) is a potentially life-threatening condition that ranks among the diseases with the highest global burden. Despite i...

EEG-CMT: Spatial-Temporal Representation of EEG for Emotion Recognition Using Convolutional Neural Networks and Vision Transformers.

Background Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional...

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach.

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR)...

A Deep Learning Approach for Analyzing Brainwave Signals During Audio Meditation in Expectant Mothers: Variational Mode Decomposition with a CNN-BiLSTM Model.

Meditation has increasingly been recognized as a helpful non-pharmacological intervention to treat psychological stress, anxiety, and depression durin...

Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence imaging.

BACKGROUND: The adult mammalian cerebral cortex has a vertical laminar organization consisting of six neuronal layers, with each layer subserving a sp...

A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting: Protocol for the ATMOSPHERE Study.

BACKGROUND: Epilepsy is a chronic neurological disorder marked by recurrent and apparently unpredictable seizures and associated with premature death,...

Research on epilepsy detection and recognition based on the combination of time frequency transform and deep learning model.

To improve the detection performance of epileptic electroencephalogram (EEG) signals and address their non-stationary characteristics,this paper compa...

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.

Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts severe pressure on healthcare systems. Early detection...

Independent evaluation of deep learning models for detecting focal cortical dysplasia.

The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) ...

MAGCANet: A multiscale adaptive graph-convolutional attention network for MI-EEG decoding.

OBJECTIVE: Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios and pronounced inter-subject variability. Althoug...

Interrater Reliability of Electroencephalogram Interpretations in Febrile Comatose African Children: Implications for Automated Interpretation Algorithm Creation.

Electroencephalography (EEG) is a diagnostic and prognostic tool used worldwide in the clinical care of comatose patients. Scalability of EEG use in r...

An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection.

Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and Mind. Current methods for measuring and identifying...

Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.

Artificial intelligence (AI) has emerged as a transformative force in neurology, offering unprecedented potential to enhance diagnostic precision, str...

Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.

PURPOSE: In the context of a multi-speaker "cocktail party" scenario where listeners selectively focus on specific speakers, human auditory attention ...

Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.

PURPOSE: This study aims to investigate differences in functional connectivity between patients on the ictal-interictal continuum (IIC) with nonconvul...

Temporal and Cell-Specific Regulation of Synaptic Homeostasis by the Chromatin Remodeler Chd1.

Disruptions in chromatin remodelers and synaptic proteins represent major genetic risk factors for autism spectrum disorder (ASD), yet how these disti...

Epilepsy: Modern Treatment Approaches, Clinical Aspects, and Patent Status.

Epilepsy is a chronic neurological disorder causing recurrent seizures. Improved diagnosis and management, including high-resolution imaging, genetic ...