Neurology

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.

Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological c...

Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease.

The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing si...

Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.

As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabi...

Caregiver burden in stroke inpatients: a randomized study comparing robot-assisted gait training and conventional therapy.

The effects of caregiver burden during the inpatient rehabilitation period have not yet been investigated. The purpose of this study was to evaluate t...

Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters.

Low back pain which is caused by the abnormal spinal alignment is one of the most common musculoskeletal symptom and, consequently, is the reason for ...

Monitoring behavioral symptoms of dementia using activity trackers.

Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) an...

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography.

In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security pro...

Artificial intelligence and deep learning in glaucoma: Current state and future prospects.

Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmo...

Reliability and accuracy of EEG interpretation for estimating age in preterm infants.

OBJECTIVES: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of funct...

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accord...

Accurate detection of spontaneous seizures using a generalized linear model with external validation.

OBJECTIVE: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and t...

Effects of Exoskeletal Lower Limb Robot Training on the Activities of Daily Living in Stroke Patients: Retrospective Pre-Post Comparison Using Propensity Score Matched Analysis.

PURPOSE: There is limited evidence of gait training using newly developed exoskeletal lower limb robot called Hybrid Assistive Limb (HAL) on the funct...

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neur...

Stroke prognostication for discharge planning with machine learning: A derivation study.

Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The s...

Tele-robotics and artificial-intelligence in stroke care.

In the last forty years, the field of medicine has experienced dramatic shifts in technology-enhanced surgical procedures - from its initial use in 19...

Predicting Alzheimer's disease progression using deep recurrent neural networks.

Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. I...

Test-Retest Reliability of Kinematic Assessments for Upper Limb Robotic Rehabilitation.

Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize motor recovery following stroke. However, few studies ...

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning.

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have...

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals.

Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources ...

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish th...