Neurology

A robotic neck brace to characterize head-neck motion and muscle electromyography in subjects with amyotrophic lateral sclerosis.

OBJECTIVE: This paper presents the first study where a dynamic neck brace was used to characterize the head motion of ALS patients while concurrently recording the surface electromyography (EMG) of the neck muscles.

Social Media Surveillance of Multiple Sclerosis Medications Used During Pregnancy and Breastfeeding: Content Analysis.

BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease occurring mostly in women of childbearing age. Pregnant women with MS are usually excluded from clinical trials; as users of the internet, however, they are actively engaged in threads and forums on social media. Social media provides the potential to explore real-world patient experiences and concerns about the use of medicinal...

Automatic extraction and assessment of lifestyle exposures for Alzheimer's disease using natural language processing.

INTRODUCTION: Previous biomedical studies identified many lifestyle exposures that could possibly represent risk factors for dementia in general or de...

Exploiting Graphoelements and Convolutional Neural Networks with Long Short Term Memory for Classification of the Human Electroencephalogram.

The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as show...

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

About 29.8 million people worldwide had been diagnosed with Alzheimer's disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, ...

Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network.

Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure ...

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network.

Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizur...

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically re...

Clinical effects of robot-assisted gait training and treadmill training for Parkinson's disease. A randomized controlled trial.

BACKGROUND: Although gait disorders strongly contribute to perceived disability in people with Parkinson's disease, clinical trials have failed to ide...

Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.

Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since n...

Telepresence robots: Encouraging interactive communication between family carers and people with dementia.

OBJECTIVE: The aim of the study was to explore the feasibility of using telepresence robots to encourage interactive communication in dementia care, f...

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, m...

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of no...

Robot-assisted gait training for balance and lower extremity function in patients with infratentorial stroke: a single-blinded randomized controlled trial.

BACKGROUND: Balance impairments are common in patients with infratentorial stroke. Although robot-assisted gait training (RAGT) exerts positive effect...

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis.

The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasing...

Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis.

BACKGROUND: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHC...

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

OBJECTIVE: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement amo...

Spread of α-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis.

Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected netw...

Programmable coupled oscillators for synchronized locomotion.

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the ro...

Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice.

. Robot-assisted therapy provides high-intensity arm rehabilitation that can significantly reduce stroke-related upper extremity (UE) deficits. Motor ...