Neurology

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the featur...

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images.

BACKGROUND AND OBJECTIVES: Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computat...

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripap...

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To ...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-ba...

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach.

Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the br...

Deep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images.

White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant im...

Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network.

A neuromorphic network composed of silver nanowires coated with TiO is found to show certain parallels with neural networks in nature such as biologic...

Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, a...

Sleep stage classification for child patients using DeConvolutional Neural Network.

Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort ha...

The Positive Effect of MiR1 Antagomir on Ischemic Neurological Disorders Via Changing the Expression of Bcl-w and Bad Genes.

INTRODUCTION: MicroRNAs (miRNAs or miRs) are non-coding RNAs. Studies have shown that miRNAs are expressed aberrantly in stroke. The miR1 enhances isc...

Wearable hip-assist robot modulates cortical activation during gait in stroke patients: a functional near-infrared spectroscopy study.

BACKGROUND: Gait dysfunction is common in post-stroke patients as a result of impairment in cerebral gait mechanism. Powered robotic exoskeletons are ...

Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.

Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characteriz...

A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network.

INTRODUCTION: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG)...

Population coding in the cerebellum: a machine learning perspective.

The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer...

Effects of selectively assisting impaired subtasks of walking in chronic stroke survivors.

BACKGROUND: Recently developed controllers for robot-assisted gait training allow for the adjustment of assistance for specific subtasks (i.e. specifi...

Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG.

Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively col...

Robot-Assisted Arm Training versus Therapist-Mediated Training after Stroke: A Systematic Review and Meta-Analysis.

BACKGROUND: More than two-thirds of stroke patients have arm motor impairments and function deficits on hospital admission, leading to diminished qual...

A Novel Inflatable Actuator Based on Simultaneous Eversion Retraction.

Inflatable robotics is a promising area for the deployment of low-cost structures that are easy to transport and deploy while allowing safe interactio...

Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia.

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Th...