AIMC Topic: Parkinson Disease

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Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

Scientific reports
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...

Metric learning for Parkinsonian identification from IMU gait measurements.

Gait & posture
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variabilit...

Analysis of facial expressions in parkinson's disease through video-based automatic methods.

Journal of neuroscience methods
BACKGROUND: The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is the study of facial bradykinesia in Parkinson's disease (PD), which is a major motor sign of this neu...

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer.

PloS one
Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map ...

Robotic-assisted gait training in Parkinson's disease: a three-month follow-up randomized clinical trial.

The International journal of neuroscience
PURPOSE: The aim of this study was to evaluate the efficacy of a robotic-assisted gait training (RAGT), together with a conventional exercise program (CEP), to improve PD ambulation, as compared to standard gait training.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

Biomedical engineering online
BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis m...

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery.

Movement disorders : official journal of the Movement Disorder Society
BACKGROUND: Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the S...

Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clin...

Parkinson's disease classification using gait analysis via deterministic learning.

Neuroscience letters
Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to ...

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

Sensors (Basel, Switzerland)
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 in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed...