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Parkinson Disease

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Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.

Sensors (Basel, Switzerland)
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate...

Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease.

Neuroscience letters
BACKGROUND: Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and clas...

High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks.

Scientific reports
Patients with advanced Parkinson's disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recordi...

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

CNS neuroscience & therapeutics
BACKGROUND: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreo...

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

International journal of medical informatics
OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk...

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

Journal of healthcare engineering
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disord...

Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning.

Gait & posture
BACKGROUND: Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson's disease (PD). It is critical that we are able to do this accurately and as early as possible in o...

LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.

Journal of neuroscience methods
BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is oft...

A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).

Medical physics
PURPOSE: Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptibility induced tissue contrast and SNR visualization of the SN pars compacta (SNPC) using conventiona...