Neurology

Prediction of future cognitive impairment among the community elderly: A machine-learning based approach.

The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluati...

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson's Disease in 3D Nuclear Imaging Data.

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we ...

Robot-assisted Therapy for the Upper Limb after Cervical Spinal Cord Injury.

Tetraplegia resulting from cervical injury is the most frequent neurologic category after spinal cord injury and causes substantial disability. The re...

A Battery-Powered Ankle Exoskeleton Improves Gait Mechanics in a Feasibility Study of Individuals with Cerebral Palsy.

Neuromuscular impairment associated with cerebral palsy (CP) often leads to life-long walking deficits. Our goal was to evaluate the ability of a nove...

Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study.

OBJECTIVE: Motor function of chronic stroke survivors is generally accessed using clinical motor assessments. These motor assessments are partially su...

A motor learning therapeutic intervention for a child with cerebral palsy through a social assistive robot.

Children with cerebral palsy have difficulty to sit, stand, walk, run and jump independently. Therapy is an important factor in improving these aspec...

Deep learning for electroencephalogram (EEG) classification tasks: a review.

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g...

A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.

Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset...

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at r...

Deep learning for waveform identification of resting needle electromyography signals.

OBJECTIVE: Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning al...

Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning.

INTRODUCTION: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, id...

Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and mu...

Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD...

EEG-based mild depression recognition using convolutional neural network.

Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infanc...

Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and gr...

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigate...

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index t...

Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation.

In this paper, we present a deep learning framework "Rehab-Net" for effectively classifying three upper limb movements of the human arm, involving ext...

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuatin...

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional...