Neurology

Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.

Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A n...

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature ...

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively imp...

Early robot-assisted gait retraining in non-ambulatory patients with stroke: a single blind randomized controlled trial.

BACKGROUND: Restoration of walking function is a primary concern of neurorehabilitation with respect to the aspired social and vocational reintegratio...

Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field.

Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as ...

Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.

The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and...

Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.

Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an...

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of sym...

Effectiveness of Superficial Radial Nerve Block on Pain, Function and Quality of Life in Patients With Hand Osteoarthritis: A Prospective, Randomized and Controlled Single-Blind Study.

OBJECTIVES: This study aims to investigate the effects of ultrasound-guided superficial branch of the radial nerve block on pain, function and quality...

Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach.

PURPOSE: To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using...

Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing l...

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitat...

Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG.

This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial...

Thalamocortical dysrhythmia detected by machine learning.

Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, t...

Development of a New Robotic Ankle Rehabilitation Platform for Hemiplegic Patients after Stroke.

A large amount of hemiplegic survivors are suffering from motor impairment. Ankle rehabilitation exercises act an important role in recovering patient...

Spine morphology and energetics: how principles from nature apply to robotics.

Inspired by the locomotive advantages that an articulated spine enables in quadrupedal animals, we explore and quantify the energetic effect that an a...

Rest tremor quantification based on fuzzy inference systems and wearable sensors.

BACKGROUND: Currently the most consistent, widely accepted and detailed instrument to rate Parkinson's disease (PD) is the Movement Disorder Society s...

ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms.

We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as hand tremors and nonvolu...

A convolutional neural network to filter artifacts in spectroscopic MRI.

PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiat...