Neurology

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.

Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.

BACKGROUND: Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and interventions. Analyzing electronic health records (EHRs) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to c...

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bon...

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.

Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninva...

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach.

This article addresses the problem of estimating brain effective connectivity from electroencephalogram (EEG) signals using a Granger causality (GC) c...

Comparison of robot-assisted versus fluoroscopy-assisted minimally invasive transforaminal lumbar interbody fusion for degenerative lumbar spinal diseases: 2-year follow-up.

This study was performed to prospectively compare the clinical and radiographic outcomes between robot-assisted minimally invasive transforaminal lumb...

Adjustable Parameters and the Effectiveness of Adjunct Robot-Assisted Gait Training in Individuals with Chronic Stroke.

The aims of this study were (1) to compare the effect of robot-assisted gait orthosis (RAGO) plus conventional physiotherapy with the effect of conven...

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based B...

EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our imple...

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine in...

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or...

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality.

Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical applica...

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.

IMPORTANCE: Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use...

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that...

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data.

Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve...

Closed-loop transcranial ultrasound stimulation with a fuzzy controller for modulation of motor response and neural activity of mice.

. We propose a closed-loop transcranial ultrasound stimulation (TUS) with a fuzzy controller to realize real-time and precise control of the motor res...

Soft robotics and functional electrical stimulation advances for restoring hand function in people with SCI: a narrative review, clinical guidelines and future directions.

BACKGROUND: Recovery of hand function is crucial for the independence of people with spinal cord injury (SCI). Wearable devices based on soft robotics...

SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy.

OBJECTIVE: Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG)...

Robot-Assisted Radical Prostatectomy After Prior Transurethral Resection of Prostate: An Analysis of Perioperative, Functional, Pathologic, and Oncologic Outcomes.

We performed a retrospective comparison of surgical, oncologic, and functional outcomes after robot-assisted radical prostatectomy between patients w...

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by r...