Latest AI and machine learning research in neurology for healthcare professionals.
INTRODUCTION: The author introduced a symptomatic cerebral vasospasm (SCV) prediction model built with freeware based on a 91-patient dataset. In a prospective test group of 22 patients at the same hospital, this model outperformed logistic regression models in vasospasm prediction on the basis of the same datasets. One of the model's limitations was a question of reproducibility in other centers....
The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task...
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (...
OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEE...
Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer ...
RNA editing is increasingly recognized as a molecular mechanism regulating RNA activity and recoding proteins. Here we surveyed the global landscape o...
BACKGROUND: Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as...
The ubiquitous is an important and troublesome pathogen of nosocomial infection. Multidrug-resistant meningitis is very difficult to treat, usually ...
Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance ...
Ankle movement training is important in motor recovery post stroke and early intervention is critical to stroke rehabilitation. However, acute stroke ...
Emulation of biological synapses is an important step toward construction of large-scale brain-inspired electronics. Despite remarkable progress in em...
BACKGROUND: Robotic neurorehabilitation aims at promoting the recovery of lost function after neurological injury by leveraging strategies of motor le...
The formation and accumulation of advanced glycation end-products (AGEs) are implicated in several chronic human illnesses including type-2 diabetes,...
The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects...
OBJECTIVE: To develop a real-time monitoring system that has the potential to guide the titration of anesthetic agents in the treatment of pediatric s...
Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this ...
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analy...
Powered robotic exoskeletons are a potential intervention for gait rehabilitation in stroke to enable repetitive walking practice to maximize neural r...
BACKGROUND: An unsettled question in the use of robotics for post-stroke gait rehabilitation is whether task-specific locomotor training is more effec...
BACKGROUND: Robot assisted gait training (RAGT) is considered to be a promising approach for improving gait-related gross motor function of children a...