Neurology

Effectiveness of robot-assisted gait training in children with cerebral palsy: a bicenter, pragmatic, randomized, cross-over trial (PeLoGAIT).

BACKGROUND: Walking ability is a priority for many children with cerebral palsy (CP) and their parents when considering domains of importance regarding treatment interventions. Partial body-weight supported treadmill training has become an established therapeutic treatment approach to address this demand. Further, new robotic rehabilitation technologies have increasingly been implemented in the cl...

Metric learning for Parkinsonian identification from IMU gait measurements.

Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with t...

Using fuzzy logic for diagnosis and classification of spasticity.

BACKGROUND/AIM: Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate ...

Analysis of facial expressions in parkinson's disease through video-based automatic methods.

BACKGROUND: The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is t...

Interactive Exploration for Continuously Expanding Neuron Databases.

This paper proposes a novel framework to help biologists explore and analyze neurons based on retrieval of data from neuron morphological databases. I...

Kinesthetic deficits after perinatal stroke: robotic measurement in hemiparetic children.

BACKGROUND: While sensory dysfunction is common in children with hemiparetic cerebral palsy (CP) secondary to perinatal stroke, it is an understudied ...

A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data.

Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as ...

Evolving Network Model That Almost Regenerates Epileptic Data.

In many realistic networks, the edges representing the interactions between nodes are time varying. Evidence is growing that the complex network that ...

AN ANALYSIS OF TWO COMMON REFERENCE POINTS FOR EEGS.

Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of elec...

SEMI-AUTOMATED ANNOTATION OF SIGNAL EVENTS IN CLINICAL EEG DATA.

To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in hea...

Sensory tractography and robot-quantified proprioception in hemiparetic children with perinatal stroke.

Perinatal stroke causes most hemiparetic cerebral palsy, resulting in lifelong disability. We have demonstrated the ability of robots to quantify sens...

Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging.

Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitiv...

Intensity-based 2D 3D registration for lead localization in robot guided deep brain stimulation.

Intraoperative assessment of lead localization has become a standard procedure during deep brain stimulation surgery in many centers, allowing immedia...

In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury.

BACKGROUND: Although commercially available activity trackers can aid in tracking therapy and recovery of patients, most devices perform poorly for pa...

Robotic-assisted gait training improves walking abilities in diplegic children with cerebral palsy.

The robotic-assisted gait training therapy (RAGT), based on intensity and repetition of movement, presents beneficial effects on recovery and improvem...

Predicting DPP-IV inhibitors with machine learning approaches.

Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like pep...

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial...

ArmAssist Robotic System versus Matched Conventional Therapy for Poststroke Upper Limb Rehabilitation: A Randomized Clinical Trial.

The ArmAssist is a simple low-cost robotic system for upper limb motor training that combines known benefits of repetitive task-oriented training, gre...

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study.

Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their d...

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug...