Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Neurovascular multiparametric MRI defines epileptogenic and seizure propagation regions in experimental mesiotemporal lobe epilepsy.

OBJECTIVE: Improving the identification of the epileptogenic zone and associated seizure-spreading r...

Uncovering the structure of clinical EEG signals with self-supervised learning.

Supervised learning paradigms are often limited by the amount of labeled data that is available. Thi...

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical c...

Efficient use of clinical EEG data for deep learning in epilepsy.

OBJECTIVE: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram...

Identification of focal epilepsy by diffusion tensor imaging using machine learning.

OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffus...

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient mo...

Semi-dilated convolutional neural networks for epileptic seizure prediction.

Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epil...

Multitask Feature Learning Meets Robust Tensor Decomposition for EEG Classification.

In this article, we study a tensor-based multitask learning (MTL) method for classification. Taking ...

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirem...

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels.

BACKGROUND: In clinical practice, EEGs are assessed visually. For practical reasons, recordings ofte...

Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach.

Patients with stroke can experience a drastic change in their body representation (BR), beyond the p...

A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

Emotion recognition based on electroencephalograms has become an active research area. Yet, identify...

External validation of automated focal cortical dysplasia detection using morphometric analysis.

OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but ...

Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving ...

A study on CNN image classification of EEG signals represented in 2D and 3D.

The novelty of this study consists of the exploration of multiple new approaches of data pre-process...

Hybrid manifold-deep convolutional neural network for sleep staging.

Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, o...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Modern motor imagery (MI)-based brain computer interface systems often entail a large number of elec...

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition.

Emotion recognition has a wide range of potential applications in the real world. Among the emotion ...

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