Neurology

Development of machine learning algorithms for prediction of mortality in spinal epidural abscess.

BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learni...

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network.

An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements,...

Comparison of machine learning models for seizure prediction in hospitalized patients.

OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify l...

Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia.

BACKGROUND: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support th...

Dynamics reconstruction and classification via Koopman features.

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and b...

Effect of EMG-biofeedback robotic-assisted body weight supported treadmill training on walking ability and cardiopulmonary function on people with subacute spinal cord injuries - a randomized controlled trial.

BACKGROUND: Body weight supported treadmill training (BWSTT) is a frequently used approach for restoring the ability to walk after spinal cord injury ...

Brain wave classification using long short-term memory network based OPTICAL predictor.

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of...

Automated detection of third molars and mandibular nerve by deep learning.

The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and s...

Exoskeletons for Personal Use After Spinal Cord Injury.

Before the development of robotic exoskeletons, mobility options beyond a wheelchair were very limited for most people lacking leg movement due to spi...

Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies.

The differentiation of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) using brain perfusion single photon emission tomography is import...

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning tec...

Diagnosis of Alzheimer's disease with Sobolev gradient-based optimization and 3D convolutional neural network.

Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early d...

Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more like...

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as pr...

Serum N-Glycosylation in Parkinson's Disease: A Novel Approach for Potential Alterations.

In this study, we present the application of a novel capillary electrophoresis (CE) method in combination with label-free quantitation and support vec...

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals.

Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challen...

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity.

Measuring spasticity is important in treatment planning and determining efficacy after treatment. However, the current tool used in clinical settings ...

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimatel...

Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.

BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, s...

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia.

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electr...