Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Regulation01:37

Neural Regulation

34.6K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
34.6K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.2K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.2K
Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

2.2K
Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
2.2K
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

1.4K
Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
1.4K
Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

21
Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of...
21
Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

22
Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...
22

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

OPTIFARM: Benchmarking YOLO Architectures for Location-Robust Potato Quality Detection.

Foods (Basel, Switzerland)·2026
Same author

Using a Region-Based Convolutional Neural Network (R-CNN) for Potato Segmentation in a Sorting Process.

Foods (Basel, Switzerland)·2025
Same author

Predicting Corn Moisture Content in Continuous Drying Systems Using LSTM Neural Networks.

Foods (Basel, Switzerland)·2025
Same author

Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.

Sensors (Basel, Switzerland)·2025
Same author

Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis.

Sensors (Basel, Switzerland)·2024
Same author

Optimization of Chaboche Material Parameters with a Genetic Algorithm.

Materials (Basel, Switzerland)·2023

Related Experiment Video

Updated: Apr 28, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

16.7K

Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks.

Lucijano Berus1, Simon Klancnik2, Miran Brezocnik3

  • 1Intelligent Manufactoring Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, Maribor 2000, Slovenia. lucijano.berus@um.si.

Sensors (Basel, Switzerland)
|December 23, 2018
PubMed
Summary

Artificial neural networks (ANNs) show promise for predicting Parkinson's disease (PD) using voice features. Optimal results were achieved with specific feature selection methods, reaching 86.47% accuracy in diagnosis.

Keywords:
Parkinson’s diseaseartificial neural networksfeature selectionvoice recognition

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

745

Related Experiment Videos

Last Updated: Apr 28, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

16.7K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

745

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Neurology

Background:

  • Parkinson's disease (PD) diagnosis relies on clinical symptoms, often leading to delayed detection.
  • Voice analysis offers a non-invasive method for potential early PD detection.
  • Artificial neural networks (ANNs) are increasingly utilized for complex prediction tasks.

Purpose of the Study:

  • To evaluate the efficacy of various feed-forward artificial neural networks (ANNs) for Parkinson's disease (PD) prediction.
  • To assess the impact of different feature selection techniques on diagnostic accuracy.
  • To identify the most informative voice features for PD detection.

Main Methods:

  • Utilized multiple feed-forward artificial neural networks (ANNs) with diverse configurations.
  • Extracted features from 26 distinct voice samples per individual.
  • Applied feature selection methods including Pearson's and Kendall's correlation coefficients, principal component analysis (PCA), and self-organizing maps (SOMs).
  • Validated results using the leave-one-subject-out (LOSO) cross-validation scheme.

Main Results:

  • Multiple ANNs demonstrated high classification accuracy for PD diagnosis even without feature selection.
  • Kendall's correlation coefficient-based feature selection yielded the best performance, identifying key voice features.
  • A fine-tuned neural network achieved a test accuracy of 86.47% for PD prediction.

Conclusions:

  • Artificial neural networks are effective tools for Parkinson's disease diagnosis using voice data.
  • Feature selection, particularly using Kendall's correlation coefficient, can enhance diagnostic accuracy.
  • Voice analysis combined with ANNs presents a promising avenue for non-invasive PD detection.