Neurology

Defining a multimodal signature of remote sports concussions.

Sports-related concussions lead to persistent anomalies of the brain structure and function that interact with the effects of normal ageing. Although post-mortem investigations have proposed a bio-signature of remote concussions, there is still no clear in vivo signature. In the current study, we characterized white matter integrity in retired athletes with a history of remote concussions by condu...

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning.

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores...

Machine Learning Methods to Predict Diabetes Complications.

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decisio...

Toward Multimodal Human-Robot Interaction to Enhance Active Participation of Users in Gait Rehabilitation.

Robotic exoskeletons for physical rehabilitation have been utilized for retraining patients suffering from paraplegia and enhancing motor recovery in ...

Experimental evaluation of a novel robotic hospital bed mover with omni-directional mobility.

Bed pushing during patient transfer is one of the most physically demanding and yet common tasks in the hospital setting. Powered bed movers have been...

A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in ...

Auditory prediction errors as individual biomarkers of schizophrenia.

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to devel...

Towards Improved Design and Evaluation of Epileptic Seizure Predictors.

OBJECTIVE: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approac...

A Neuromuscular Electrical Stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke.

BACKGROUND: It is a challenge to reduce the muscular discoordination in the paretic upper limb after stroke in the traditional rehabilitation programs...

Automatic Assessment of a Rollator-User's Condition During Rehabilitation Using the i-Walker Platform.

Patient condition during rehabilitation has been traditionally assessed using clinical scales. These scales typically require the patient and/or the c...

Novel Screening Tool for Stroke Using Artificial Neural Network.

BACKGROUND AND PURPOSE: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time w...

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting di...

Anticonvulsant effect of ethanolic extract of L. leaves on pentylenetetrazol induced seizure in mice.

(CA) rhizomes have demonstrated different properties on nervous system. However, the leaves still have not studied to treat epilepsy. The aim of this...

Selected essential oils inhibit key physiological enzymes and possess intracellular and extracellular antimelanogenic properties in vitro.

Essential oils (EOs) extracted from six medicinal herbs and food plants [Cinnamomum zeylanicum (CZ), Psiadia arguta (PA), Psiadia terebinthina (PT), C...

An automatic non-invasive method for Parkinson's disease classification.

BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various...

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.

BACKGROUND: The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients.

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination,...

Quantification of task-dependent cortical activation evoked by robotic continuous wrist joint manipulation in chronic hemiparetic stroke.

BACKGROUND: Cortical damage after stroke can drastically impair sensory and motor function of the upper limb, affecting the execution of activities of...

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these ...

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classifi...