Neurology

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of pr...

Technology-assisted stroke rehabilitation in Mexico: a pilot randomized trial comparing traditional therapy to circuit training in a Robot/technology-assisted therapy gym.

BACKGROUND: Stroke rehabilitation in low- and middle-income countries, such as Mexico, is often hampered by lack of clinical resources and funding. To provide a cost-effective solution for comprehensive post-stroke rehabilitation that can alleviate the need for one-on-one physical or occupational therapy, in lower and upper extremities, we proposed and implemented a technology-assisted rehabilitat...

A Novel EMG Interface for Individuals With Tetraplegia to Pilot Robot Hand Grasping.

This paper introduces a new human-machine interface for individuals with tetraplegia. We investigated the feasibility of piloting an assistive device ...

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients.

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop ...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals i...

Test-retest reliability and four-week changes in cardiopulmonary fitness in stroke patients: evaluation using a robotics-assisted tilt table.

BACKGROUND: Exercise testing devices for evaluating cardiopulmonary fitness in patients with severe disability after stroke are lacking, but we have a...

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a ...

Serum Leptin Levels and Cognition in Parkinson's Disease Patients.

INTRODUCTION: To investigate the relationship between serum leptin levels and cognition in Parkinson's disease (PD) patients.

Coexistence and local μ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays.

In this paper, the coexistence and dynamical behaviors of multiple equilibrium points are discussed for a class of memristive neural networks (MNNs) w...

Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG.

Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic elec...

Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia.

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroenc...

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to...

Quantification of rutin in rat's brain by UHPLC/ESI-Q-TOF-MS/MS after intranasal administration of rutin loaded chitosan nanoparticles.

Rutin (RT), an antioxidant drug, has been utilized to treat cerebral ischemia hence a sensitive quantification method for estimation of RT in brain ho...

Change in quality of life in older people with dementia participating in Paro-activity: a cluster-randomized controlled trial.

AIM: The aim of this study was to investigate effects of robot-assisted group activity with Paro on quality of life in older people with dementia.

Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines.

BACKGROUND: Myoelectric signals offer significant insights in interpreting the motion intention and extent of effort involved in performing a movement...

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms.

Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It...

Ability of electrical stimulation therapy to improve the effectiveness of robotic training for paretic upper limbs in patients with stroke.

We investigated whether untriggered neuromuscular electrical stimulation (NMES) can increase the effectiveness of shoulder and elbow robotic training ...

Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is...

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical...

Ten problems and solutions when predicting individual outcome from lesion site after stroke.

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basi...