Neurology

A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors.

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpr...

Development and Validation of a Kinematically Accurate Upper-Limb Exoskeleton Digital Twin for Stroke Rehabilitation.

Rehabilitation robotics combined with virtual reality using head-mounted displays enable naturalistic, immersive, and motivating therapy for people after stroke. There is growing interest in employing digital twins in robotic neurore-habilitation, e.g., in telerehabilitation for virtual coaching and monitoring, as well as in immersive virtual reality applications. However, the kinematic matching o...

Influence of Robotic Therapy on Severe Stroke Patients.

Robotic rehabilitation has emerged as a promising approach to enhance motor recovery after stroke, but there is limited knowledge about its efficacy i...

Diagnostic Detection of H7N3 Low Pathogenicity Avian Influenza in a Commercial Game Bird Flock.

Low pathogenicity avian influenza (LPAI) H7N3 was diagnosed in a flock of commercially raised Japanese quail () breeder hens. The birds were submitted...

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stag...

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence.

IMPORTANCE: Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the wo...

[Robot-assisted Extended Thymectomy via a Bilateral Approach for Patients with Myasthenia Gravis].

Extended thymectomy is a procedure to remove the thymus gland and surrounding adipose tissue, while the traditional approach via a median sternotomy, ...

Feature extraction of time series data on functional near-infrared spectroscopy and comparison of deep learning performance for classifying patients with Alzheimer's-related mild cognitive impairment: a post-hoc analysis of a diagnostic interventional trial.

OBJECTIVE: This study aimed to define a method of classifying patients with mild cognitive impairment caused by Alzheimer's disease by the retrieval o...

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait.

OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatr...

An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external device...

An EEG-based brain-computer interface for real-time multi-task robotic control.

The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical ...

Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot.

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neurom...

Multi-Grasp Classification for the Control of Robot Hands Employing Transformers and Lightmyography Signals.

The increasing use of smart technical devices in our everyday lives has necessitated the use of muscle-machine interfaces (MuMI) that are intuitive an...

Real-Time Epileptic Seizure Detection Based on Deep Learning.

Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, ...

EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network.

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devi...

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data.

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are wid...

2D Wavelet-Scalogram Deep-Learning for Seizures Pattern Identification in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep.

Neonatal seizures after an hypoxic-ischemic (HI) event in preterm newborns can contribute to neural injury and cause impaired brain development. Prete...

Predicting Dementia Risk for Elderly Community Dwellers in Primary Care Services Using Subgroup-specific Prediction Models.

Early detection of individuals with a high risk of dementia is crucial for prompt intervention and clinical care. This study aims to identify high-ris...

Primary color decoding using deep learning on source reconstructed EEG signal responses.

The brain's response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control thei...

Design of an experimental setup for delivering intracortical microstimulation in vivo via Spiking Neural Network.

Electroceutical approaches for the treatment of neurological disorders, such as stroke, can take advantage of neuromorphic engineering, to develop dev...