Parkinson's Disease

FESAEI: a fuzzy rule-based expert system for the assessment of environmental impacts : A fuzzy logic approach to impact assessment.

Currently, the method mostly used by practitioners of environmental impact assessment (EIA) is the "crisp numbers" method. Nevertheless, this arithmetic method is far away of giving correct values due to its rigidity and the lack of consideration of important aspects as the imprecision and incompleteness of data and the uncertainty that usually pervade our knowledge of environment. A more flexible...

Structural neuroimaging as clinical predictor: A review of machine learning applications.

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are ...

Activity-aware essential tremor evaluation using deep learning method based on acceleration data.

BACKGROUND: Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provid...

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons,...

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have...

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases.

Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntingto...

Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features.

BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder res...

Effect of Health and Training on Ultrasensitive Cardiac Troponin in Marathon Runners.

PURPOSE: Cardiac troponin (cTn) is the gold standard biomarker for assessing cardiac damage. Previous studies have demonstrated increases in plasma cT...

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two in...

Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.

INTRODUCTION: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment,...

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micro...

Thalamocortical dysrhythmia detected by machine learning.

Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, t...

Rest tremor quantification based on fuzzy inference systems and wearable sensors.

BACKGROUND: Currently the most consistent, widely accepted and detailed instrument to rate Parkinson's disease (PD) is the Movement Disorder Society s...

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state ...

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by ti...

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing...

Motor and psychosocial impact of robot-assisted gait training in a real-world rehabilitation setting: A pilot study.

In the last decade robotic devices have been applied in rehabilitation to overcome walking disability in neurologic diseases with promising results. R...

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson's disease prediction.

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control....

Characterization of the stimulus waveforms generated by implantable pulse generators for deep brain stimulation.

OBJECTIVE: To determine the circuit elements required to theoretically describe the stimulus waveforms generated by an implantable pulse generator (IP...