Parkinson's Disease

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease.

OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.

Olfactory Testing in Parkinson Disease and REM Behavior Disorder: A Machine Learning Approach.

OBJECTIVE: We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD).

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

INTRODUCTION: Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a val...

Effectiveness of robotic balance training on postural instability in patients with mild Parkinson's disease: A pilot, single blind, randomized controlled trial.

OBJECTIVE: To examine whether tailored robotic platform training could improve postural stability compared with conventional balance treatment in pati...

Variable Stiffness Devices Using Fiber Jamming for Application in Soft Robotics and Wearable Haptics.

Variable stiffness actuation has applications in a wide range of fields, including wearable haptics, soft robots, and minimally invasive surgical devi...

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease.

OBJECTIVE: We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease...

The role of individual neuron ion conductances in the synchronization processes of neuron networks.

The partial phase synchronization (sometimes called cooperation) of neurons is fundamental for the understanding of the complex behavior of the brain....

Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether ki...

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parki...

Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.

PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by...

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study.

PURPOSE: Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Par...

Mechanics and Morphological Compensation Strategy for Trimmed Soft Whisker Sensor.

Recent studies have been inspired by natural whiskers for a proposal of tactile sensing system to augment the sensory ability of autonomous robots. In...

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correcti...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors deter...

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficul...

Worldwide diffusion of robotic approach in general surgery.

The robotic system has overcome some laparoscopic shortcomings, such as stereoscopic 3D-magnified vision, a stable camera and flexible and tremor filt...

Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency.

BACKGROUND: Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the ...

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to ...

Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration.

Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing inte...

Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both m...