Neurology

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent postconcussive symptoms are most likely multifactorial and the underlying mechanism is not well understood, although it is clear that sleep disturbances ...

Musculoskeletal Neural Network path generator for a virtual upper-limb active controlled orthosis.

In this paper, a non-parametric model of the neuromusculoskeletal system for the biceps brachii is presented. The model serves to generate angular paths for the control of a virtual active orthosis. The path generator uses a differential neural network (DNN) identifier that obtains the reference angular position and velocities using the raw electromyographic (EMG) signals as input. The model is va...

The Effects of EMG-Based Classification and Robot Control Method on User's Neuromuscular Effort during Real-Time Assistive Hand Exoskeleton Operation.

EMG-based intention recognition and assistive device control are often developed separately, which can lead to the unintended consequence of requiring...

Characterization of multiscale logic operations in the neural circuits.

: Ever since the seminal work by McCulloch and Pitts, the theory of neural computation and its philosophical foundation known as 'computationalism' ha...

[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia dep...

Minimally invasive thymectomy for myasthenia gravis favours left-sided approach and low severity class.

OBJECTIVES: Complete thymectomy is a key component of the optimal treatment for myasthenia gravis. Unilateral, minimally invasive approaches are incre...

Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography.

STUDY OBJECTIVES: To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between ...

[Da Vinci robot-assisted pylorus and vagus nerve-preserving partial gastrectomy for gastric cancer].

To investigate the safety and feasibility of Da Vinci robot-assisted pylorus and vagus nerve-preserving partial gastrectomy for gastric cancer. In t...

Towards Interpretable Machine Learning in EEG Analysis.

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the i...

Future Directions in Robotic Neurosurgery.

In this paper, we highlight promising technologies in each phase of a robotic neurosurgery operation, and identify key factors affecting how quickly t...

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been...

Effect of robotic-assisted gait training on functional status, walking and quality of life in complete spinal cord injury.

The purpose of this study was to investigate the effect of robotic-assisted gait training (RAGT) on functional status and the quality of life in patie...

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

BACKGROUND: To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of gla...

The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery : a narrative review.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthc...

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No o...

Soft robotic steerable microcatheter for the endovascular treatment of cerebral disorders.

Catheters used for endovascular navigation in interventional procedures lack dexterity at the distal tip. Neurointerventionists, in particular, encoun...

A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an ...

The structural connectome and motor recovery after stroke: predicting natural recovery.

Stroke patients vary considerably in terms of outcomes: some patients present 'natural' recovery proportional to their initial impairment (fitters), w...

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electr...

Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence.

Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of ...