Neurology

Exoskeleton and End-Effector Robots for Upper and Lower Limbs Rehabilitation: Narrative Review.

Recovery of upper and lower limbs function is essential to reach independence in daily activities in patients with upper motor neuron syndrome (UMNS). Rehabilitation can provide a guide for motor recovery influencing the neurobiology of neuronal plasticity providing controlled, repetitive, and variable patterns. Increasing therapy dosage, intensity, number of repetition, execution of task-oriented...

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person ...

[Big data and artificial intelligence for diagnostic decision support in atypical dementia].

The differential diagnosis of atypical dementia remains difficult. The use of positron emission tomography (PET) still represents the gold standard fo...

Effect of reducing assistance during robot-assisted gait training on step length asymmetry in patients with hemiplegic stroke: A randomized controlled pilot trial.

BACKGROUND: An assist-as-needed robot-assisted gait training protocol was recently developed. It allows active movement during training, but its exact...

The economic cost of robotic rehabilitation for adult stroke patients: a systematic review protocol.

The objective of this review is to identify the best available evidence on the economic cost of robotic rehabilitation for adult stroke patients to im...

A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG.

Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interacti...

Effects of newly developed compact robot-aided upper extremity training system (Neuro-X®) in patients with stroke: A pilot study.

OBJECTIVE: Robot-assisted rehabilitation therapy of the upper extremity after stroke has been studied widely; however, robotic devices remain expensiv...

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet ...

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of wh...

Editorial Comment on: Effect of Mannitol on Ultrasonographically Measured Optic Nerve Sheath Diameter as a Surrogate for Intracranial Pressure During Robot-Assisted Laparoscopic Prostatectomy with Pneumoperitoneum and the Trendelenburg Position by Jun et al.

Robotic Rehabilitation and Spinal Cord Injury: a Narrative Review.

Mobility after spinal cord injury (SCI) is among the top goals of recovery and improvement in quality of life. Those with tetraplegia rank hand functi...

Gated Recurrent Neural Networks for EMG-Based Hand Gesture Classification. A Comparative Study.

Electromyographic activities (EMG) generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing th...

Deep Learning for Medication Assessment of Individuals with Parkinson's Disease Using Wearable Sensors.

Motor fluctuations between "OFF" state (with no benefit from medication) and " ON" state (with optimum benefit from medication) are a major focus of c...

Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks.

Current sleep medicine relies on the supervised analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), elect...

EEG Processing to Discriminate Transitive-Intransitive Motor Imagery Tasks: Preliminary Evidences using Support Vector Machines.

It is known that brain dynamics significantly changes during motor imagery tasks of upper limb involving different kind of interactions with an object...

Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network.

Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild...

Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease.

Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain struct...

DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification.

We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classifica...

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exis...

Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.

Estimation of cell nuclei in images stained for the c-fos protein using immunohistochemistry (IHC) is infeasible in large image sets. Use of multiple ...