Neurology

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

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Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, ...

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RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular process...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with h...

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered E...

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective ...

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

BACKGROUND: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that...

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

Timely and accurately seizure detection is of great importance for the diagnosis and treatment of ep...

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural state...

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a hi...

Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.

BACKGROUND: Neurological complications are common in patients receiving veno-venous extracorporeal m...

Effect of robot-assisted gait training on improving cardiopulmonary function in stroke patients: a meta-analysis.

OBJECTIVE: Understanding the characteristics related to cardiorespiratory fitness after stroke can p...

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EE...

Task-Oriented Training by a Personalized Electromyography-Driven Soft Robotic Hand in Chronic Stroke: A Randomized Controlled Trial.

BACKGROUND: Intensive task-oriented training has shown promise in enhancing distal motor function am...

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review.

PURPOSE OF REVIEW: As artificial intelligence (AI) and machine learning (ML) are becoming more perva...

Microglial mediators in autoimmune Uveitis: Bridging neuroprotection and neurotoxicity.

Autoimmune uveitis, a severe inflammatory condition of the eye, poses significant challenges due to ...

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain ...

No playing around with robots? Ambivalent attitudes toward the use of Paro in elder care.

This paper explores the ways in which health care professionals, family carers, and older persons ex...

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