Neurology

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.

OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its applic...

A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training.

OBJECTIVE: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address musc...

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

BACKGROUND: Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis i...

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have...

Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.

Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The change...

Comment to «Robot-assisted pudendal neurolysis in the treatment of pudendal nerve entrapment syndrome».

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features.

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as m...

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold.

Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communica...

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases.

Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntingto...

Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural systems in the basic kinematic case of clusters of neur...

Altered Micro-RNA Regulation and Neuroprotection Activity of in Alzheimer's Disease Model.

BACKGROUND: Amyloid-β peptide (Aβ) is involved in the formation of senile plaques in Alzheimer's disease (AD), and causes neuronal cell death by induc...

Reliability, validity, and clinical feasibility of a rapid and objective assessment of post-stroke deficits in hand proprioception.

BACKGROUND: Proprioceptive function can be affected after neurological injuries such as stroke. Severe and persistent proprioceptive impairments may b...

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

BACKGROUND: Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challen...

Multicenter validation of [F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.

OBJECTIVE: F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differ...

A novel application of deep learning for single-lead ECG classification.

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach bas...

Ischemic stroke lesion segmentation using stacked sparse autoencoder.

Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantify...

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical appli...

Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy.

OBJECTIVE: An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the du...

Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features.

BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder res...