Neurology

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional ...

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

OBJECTIVE: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer-Inner plexi...

Development of a machine learning algorithm for prediction of failure of nonoperative management in spinal epidural abscess.

BACKGROUND CONTEXT: Data regarding risk of failure of nonoperative management in spinal epidural abscess (SEA) are limited. Given the potential for de...

Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting.

To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and ...

Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy ...

Assessment of pressure pain thresholds in collisions with collaborative robots.

In recent years, safety issues surrounding robots have increased in importance, as more robots are in close contact with humans, both in industrial fi...

Robot-Assisted Reaching Performance of Chronic Stroke and Healthy Individuals in a Virtual Versus a Physical Environment: A Pilot Study.

The aim of the current study was to examine the role of environment, whether virtual or physical, on robot-assisted reaching movements in chronic stro...

Predictive analytics and machine learning in stroke and neurovascular medicine.

Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost ever...

Combination of Exoskeletal Upper Limb Robot and Occupational Therapy Improve Activities of Daily Living Function in Acute Stroke Patients.

PURPOSE: Previous studies have suggested that upper limb rehabilitation using therapeutic robots improves motor function of stroke patients. However, ...

Analysis of parameters affecting blood oxygen saturation and modeling of fuzzy logic system for inspired oxygen prediction.

BACKGROUND AND OBJECTIVE: Fraction of Inspired Oxygen is one of the arbitrary set ventilator parameters which has critical influence on the concentrat...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in m...

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders.

To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental...

Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy.

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this i...

Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.

People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious acci...

An unsupervised EEG decoding system for human emotion recognition.

Emotion plays a vital role in human health and many aspects of life, including relationships, behaviors and decision-making. An intelligent emotion re...

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.

Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the ...

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks.

This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up ...

Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.

Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Elect...

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy.

Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to in...

Development of a robotic walker for individuals with cerebral palsy.

This study describes the first use of a robotic walker in youth and young adults with cerebral palsy (CP) Gross Motor Function Classification (GMFCS)...