Neurology

Computational medication regimen for Parkinson's disease using reinforcement learning.

Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by us...

Leveraging deep learning to control neural oscillators.

Modulation of the firing times of neural oscillators has long been an important control objective, with applications including Parkinson's disease, Tourette's syndrome, epilepsy, and learning. One common goal for such modulation is desynchronization, wherein two or more oscillators are stimulated to transition from firing in phase with each other to firing out of phase. The optimization of such st...

Humanoid socially assistive robots in dementia care: a qualitative study about expectations of caregivers and dementia trainers.

OBJECTIVE: To examine the expectations of informal caregivers, nurses, and dementia trainers regarding the support of (physical and psychosocial) huma...

Automated Segmentation of the Median Nerve in the Carpal Tunnel using U-Net.

Nerve area and motion in carpal tunnel syndrome (CTS) are currently under investigation in terms of prognostic potential. Therefore, there is increasi...

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identifi...

Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG.

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection o...

Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN).

The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerati...

Edge deep learning for neural implants: a case study of seizure detection and prediction.

Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilep...

A Model-Based Analysis of Supraspinal Mechanisms of Inter-Leg Coordination in Human Gait: Toward Model-Informed Robot-Assisted Rehabilitation.

Stroke survivors are often left suffering from gait instability due to hemiparesis. This gait dysfunction can lead to higher fall rates and an overall...

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-r...

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in u...

Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia.

Although progress is being made in affective computing, issues remain in enabling the effective expression of compassionate communication by healthcar...

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. ...

Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors.

OBJECTIVES: Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated th...

Effect of Robot Assisted Gait Training on Motor and Walking Function in Patients with Subacute Stroke: A Random Controlled Study.

BACKGROUND: Robot-assisted gait training has been confirmed to have beneficial effect on the rehabilitation of stroke patients. An exoskeleton robot, ...

Development of a Planar Haptic Robot With Minimized Impedance.

Several studies have reported that stroke survivors displayed improved voluntary planar movements when forces supporting the upper limb increased, and...

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in t...

Machine learning for detection of interictal epileptiform discharges.

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharge...

Getting into a "Flow" state: a systematic review of flow experience in neurological diseases.

BACKGROUND: Flow is a subjective psychological state that people report when they are fully involved in an activity to the point of forgetting time an...

Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke.

We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle-foot orthosis (AFO) in str...