Neurology

MCADNNet: Recognizing Stages of Cognitive Impairment through Efficient Convolutional fMRI and MRI Neural Network Topology Models.

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD). Early diagnosis of MCI and AD through artificial intelligence has captured considerable scholarly interest; researchers hope to develop therapies capable of slowing or halting these processes. We developed a state-of-the-art deep learning algorithm ...

Review of outcome measures in PARO robot intervention studies for dementia care.

The aim of this study was to describe interventions for PARO, as well as the outcomes evaluated and found following use of PARO, and to identify outcome measures in PARO intervention studies for older adults with dementia. Multiple databases (Web of Science, PubMed, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Cochrane, and Scopus) were searched and eight studies were included...

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.

BACKGROUND AND OBJECTIVE: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is t...

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.

OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification probl...

Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke.

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarker...

Fixation of intracapsular fracture of the femoral neck using combined peripheral nerve blocks and transthoracic echocardiography in a patient with severe obstructive hypertrophic cardiomyopathy: a case report.

BACKGROUND: Hypertrophic obstructive cardiomyopathy (HOCM) is a type of hypertrophic cardiomyopathy associated with left ventricular outflow tract ste...

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted...

Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial.

Background and Purpose- The purpose of this study was to investigate the effects of gait training with a newly developed wearable hip-assist robot on ...

Are Wearable Robots Effective for Gait Recovery After Stroke?

Reversing the Effects of a Peripheral Nerve Block with Normal Saline: A Randomised Controlled Trial.

OBJECTIVE: The objective of the present study was to determine whether or not the effects of peripheral nerve block can be reversed by flushing normal...

Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks.

UNLABELLED: The effect of untreated Obstructive Sleep Apnoea (OSA) on cerebral haemodynamics and CA impairment is an active field of research interest...

Spiking Neural Networks applied to the classification of motor tasks in EEG signals.

Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain s...

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic str...

Effects of robotic neurorehabilitation through lokomat plus virtual reality on cognitive function in patients with traumatic brain injury: A retrospective case-control study.

Traumatic brain injury (TBI) is a clinical condition characterized by damage due to a mechanical physical event, which has a devastating impact on bo...

Optimal selection of SOP and SPH using fuzzy inference system for on-line epileptic seizure prediction based on EEG phase synchronization.

Living conditions of patients with refractory epilepsy will be significantly improved by a successful prediction of epileptic seizures. A proper warni...

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can furthe...

Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.

Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated...

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests th...

A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks.

In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people wit...

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machin...