Neurology

An Alzheimers disease related genes identification method based on multiple classifier integration.

BACKGROUND AND OBJECTIVE: Alzheimers disease (AD) is a fatal neurodegenerative disease and the onset of AD is insidious. Full understanding of the AD-related genes (ADGs) has not been completed. The National Center for Biotechnology Information (NCBI) provides an AD dataset of 22,283 genes. Among these genes, 71 genes have been identified as ADGs. But there may still be underlying ADGs that have n...

Deep learning with convolutional neural networks for EEG decoding and visualization.

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets...

Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.

BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; r...

Association Between Optic Nerve Head Blood Flow Measured Using Laser Speckle Flowgraphy and Radial Arterial Pressure During Aortic Arch Surgery.

OBJECTIVE: This study was designed to investigate the association between ocular blood flow measured using laser speckle flowgraphy (LSFG) and radial ...

Use of a Robotic Seal as a Therapeutic Tool to Improve Dementia Symptoms: A Cluster-Randomized Controlled Trial.

OBJECTIVES: To test the effects of individual, nonfacilitated sessions with PARO (version 9), when compared against a look-alike plush toy and usual c...

Mixed Neural Network Approach for Temporal Sleep Stage Classification.

This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage class...

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure.

Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions tha...

Phenomenological network models: Lessons for epilepsy surgery.

The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with re...

Human interaction with robotic systems: performance and workload evaluations.

We first tested the effect of differing tactile informational forms (i.e. directional cues vs. static cues vs. dynamic cues) on objective performance ...

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (...

Robot-Applied Resistance Augments the Effects of Body Weight-Supported Treadmill Training on Stepping and Synaptic Plasticity in a Rodent Model of Spinal Cord Injury.

BACKGROUND: The application of resistive forces has been used during body weight-supported treadmill training (BWSTT) to improve walking function afte...

Robotic Gait Training for Individuals With Cerebral Palsy: A Systematic Review and Meta-Analysis.

OBJECTIVE: To identify the effects of robotic gait training practices in individuals with cerebral palsy.

AExaCTT - Aerobic Exercise and Consecutive Task-specific Training for the upper limb after stroke: Protocol for a randomised controlled pilot study.

Motor function may be enhanced if aerobic exercise is paired with motor training. One potential mechanism is that aerobic exercise increases levels of...

Robot Assisted Training for the Upper Limb after Stroke (RATULS): study protocol for a randomised controlled trial.

BACKGROUND: Loss of arm function is a common and distressing consequence of stroke. We describe the protocol for a pragmatic, multicentre randomised c...

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

BACKGROUND: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepte...

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment.

BACKGROUND AND OBJECTIVE: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from norma...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-in...

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algor...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among e...

Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.

The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 3...