Latest AI and machine learning research in seizures for healthcare professionals.
Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investigate the application of deep learning methods to electroencephalogram (EEG) signals to predict clinical remission after 6 weeks of home-based transcranial direct current stimulation (tDCS) treatment. Pre-treatment resting-state EEG acquired from 21 bip...
Internet Gaming Disorder (IGD) is officially listed as a behavioral addiction, exhibits high prevalence and has inadequate treatment efficacy. Targeting craving triggered by gaming cues represents a critical therapeutic objective. This study aimed to establish optimizing neuro-electrophysiologic biomarkers for IGD and develop a targeted neuromodulation protocol. In an exploratory study, we identif...
Biological age estimation, derived from physiological signatures such as brain activity, is emerging as a valuable biomarker for health and well-being...
To develop and validate a deep survival model (EEGSurvNet) that analyzes routine EEG to predict individual seizure risk over time, comparing its perfo...
Epilepsy affects over 50 million people worldwide, with anti-seizure medications (ASMs) as the primary treatment for seizure control. However, ASM sel...
Chronic pain is a major healthcare problem associated with maladaptive brain circuit changes - many patients are unresponsive to all available therapi...
About one third of epilepsy patients are drug-resistant. Resective surgery remains a key treatment option but depends critically on accurate identific...
Structural neuroimaging analyses require ‘research quality’ images, acquired with costly MRI acquisitions. Isotropic (3D-T1) images are desirable for ...
We thoroughly investigated the generalizability of deep learning models trained on electroencephalography (EEG) data to detect Alzheimer’s disease and...
Automated interpretation of resting-state fMRI (rs-fMRI) for epilepsy diagnosis remains a challenge. We developed a regularized transformer that model...
Epilepsy affects approximately 1% of the global population, with 30-40% of cases resistant to conventional pharmacological treatments. Current neurost...
To test the hypothesis that deep learning can decode single-trial cortical responses from electroencephalography (EEG) to individual, long-duration mi...
Fluid biomarkers are emerging as crucial markers for diagnosis and disease monitoring in neurology. Epilepsy remains an exception despite seizures bei...
Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensiona...
Objective: Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an ...
Interictal epileptiform discharges (IEDs) are reliable biomarkers in electroencephalograms for epilepsy. To automate IED detection, deep learning (DL)...
This study aimed to develop and validate a system of specialized deep lightweight convolutional neural networks (CNN) to accurately detect specific ar...
Epilepsy affects approximately 50 million people worldwide, and seizures remain difficult to predict in onset, severity, and duration. Real-time seizu...
The goal of this study is seizure detection in four class datasets for different seizure stages in epileptic patients. An early notification system is...
Accurately distinguishing between epileptic seizures (ES) and nonepileptic seizures (NES) is a significant clinical challenge that typically requires ...