Neurology

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) s...

Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks.

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) within neurons. The distribution and density of tau pathology throughout the brain is evaluated at autopsy as one component of Alzheimers disease diagnosis. Deep neural networks (DNN) have been shown to be effective in the ...

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the pr...

Deep Learning for Neuroimaging Segmentation with a Novel Data Augmentation Strategy.

Brain insults such as cerebral ischemia and intracranial hemorrhage are critical stroke conditions with high mortality rates. Currently, medical image...

Unsupervised stratification in neuroimaging through deep latent embeddings.

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as...

Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintai...

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study.

Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time con...

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of...

Classifying cross-frequency coupling pattern in epileptogenic tissues by convolutional neural network.

The phase-amplitude coupling in EEG signal of different frequencies is considered as a useful biomarker in delineating epileptogenic tissues, but some...

Impact of Encapsulation Tissue Growth on Selective Recording in Nerve Cuff Electrodes: A Simulation Study.

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals ...

Differentiating Motor Coordination and Position Sense in Children with Cerebral Palsy and Typically Developing Populations Through Robotic Assessments.

Motor function and coordination improve as children age. Robotic assessments of motor function and coordination have been shown to be repeatable, obje...

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challengin...

Investigating muscle synergies changes after rehabilitation robotics training on stroke survivors: a pilot study.

The current knowledge about muscle synergies does not clearly explain how both rehabilitation and brain plasticity act on the way they evolve after a ...

'Write' but not 'spell' Chinese characters with a BCI-controlled robot.

Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words...

Subject-specific, Impairment-based Robotic Training of Functional Upper Limb Movements.

Significant hand and upper-limb impairment is common post-stroke. Robotic training can administer a high-dose of repetitive movement training to strok...

Movement strategy and EMG activities of the upper extremity at assisted reaching exercise with a 7 DOF collaborative robot.

Recovering of upper extremity functions is important for stroke patients to perform various tasks in daily life. For better rehabilitation outcomes an...

Deep learning with long short-term memory networks for classification of dementia related travel patterns.

Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PW...

Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.

Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Tracé alternant (TA)-a char...

Automated Nerve Fibres Identification and Morphometry Analysis with Neural Network Based Tool in MATLAB.

Analyses of nerve histology are core assays in basic and applied research and even in clinical setting. Detailed report on nerve morphology may unbias...

Integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke.

We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifyi...