Neurology

Automatic brain tissue segmentation in fetal MRI using convolutional neural networks.

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segme...

Quantifying performance of machine learning methods for neuroimaging data.

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroi...

Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.

Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression a...

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term...

Robotic-Assisted Gait Training Effect on Function and Gait Speed in Subacute and Chronic Stroke Population: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The review is intended to provide the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in poststroke su...

Ascending Lipopolysaccharide-Induced Intrauterine Inflammation in Near-Term Rabbits Leading to Newborn Neurobehavioral Deficits.

BACKGROUND: Chorioamnionitis from ascending bacterial infection through the endocervix is a potential risk factor for cerebral palsy. Tetrahydrobiopte...

Effects of Transcranial Direct Current Stimulation (tDCS) Combined With Wrist Robot-Assisted Rehabilitation on Motor Recovery in Subacute Stroke Patients: A Randomized Controlled Trial.

Both transcranial direct current stimulation (tDCS) and wrist robot-assisted training have demonstrated to be promising approaches for stroke rehabili...

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electro...

Deep Learning for the Radiographic Detection of Apical Lesions.

INTRODUCTION: We applied deep convolutional neural networks (CNNs) to detect apical lesions (ALs) on panoramic dental radiographs.

Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been w...

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremo...

Applications of deep learning for the analysis of medical data.

Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other ...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

The Use of Deep Learning to Predict Stroke Patient Mortality.

The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can impro...

FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals.

Recently, pervasive sensing technologies have been widely applied to comprehensive patient monitoring in order to improve clinical treatment. Various ...

Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification.

BACKGROUND: Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control exter...

Effect of verticalization with Erigo® in the acute rehabilitation of severe acquired brain injury.

The recovery of the orthostatism after a severe acquired brain injury (sABI) is an essential objective to pursue in order to avoid the occurrence of s...

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impa...

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to r...

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in ...