Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Regulation

Neural Regulation

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.

You might also read

Related Articles

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

Sort by

Same author

JSPR-Net: A Jacobian-stabilized physics-informed residual neural network for breast cancer detection and fractional-order disease progression modeling.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Resonant recognition model-guided biophysical insights into POMC, leptin, adiponectin, BDNF, insulin, irisin, GDF-15 interactions & executing PBMT targeting leptin/BDNF circuit to attenuate obesity.

Lasers in medical science·2025

Same author

Modulation of Angiogenesis through Endogenous Leptin Signalling: Network Pharmacology and Resonant Recognition Model Approaches in Cardiovascular Therapy.

Current vascular pharmacology·2025

Same author

Analyzing VEGFA/VEGFR1 Interaction: Application of the Resonant Recognition Model-Stockwell Transform Method to Explore Potential Therapeutics for Angiogenesis-Related Diseases.

The protein journal·2024

Same author

Computational models for prediction of protein-protein interaction in rice and <i>Magnaporthe grisea</i>.

Frontiers in plant science·2023

Same author

Identification of characteristics frequency and hot-spots in protein sequence of COVID-19 disease.

Biomedical signal processing and control·2022

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Parkinson's Disease Detection Using Hybrid Siamese Neural Network and Support Vector Machine in Multilingual Voice

Pandit Vivek Kumar Pandey¹, Sitanshu Sekhar Sahu¹

¹Department of ECE, Birla Institute of Technology, Mesra, Ranchi, India.

Journal of Voice : Official Journal of the Voice Foundation

|August 5, 2025

Summary

This study introduces a hybrid Siamese Neural Network (SNN) with support vector machine (SVM) for detecting Parkinson's disease (PD) using voice analysis across multiple languages. The method achieves high accuracy, offering a promising solution for early PD detection in diverse linguistic settings.

Keywords:

Euclidian distance Parkinson's disease Siamese neural network Speech signal Support vector machine

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 12, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

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

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

Published on: December 15, 2023

Area of Science:

Neurology
Computational Linguistics
Machine Learning

Background:

Parkinson's disease (PD) is a neurodegenerative disorder impacting motor functions, including speech, early in its progression.
Voice analysis is a crucial, non-invasive marker for early PD detection.
Existing PD detection studies predominantly focus on single languages, neglecting multilingual challenges like acoustic variations and data scarcity.

Purpose of the Study:

To develop and evaluate a hybrid Siamese Neural Network (SNN) with a support vector machine (SVM) for robust Parkinson's disease detection across multiple languages.
To address the challenges posed by language-specific acoustic variations and limited datasets in multilingual PD detection scenarios.
To leverage contrastive learning for capturing PD and healthy voice patterns in diverse acoustic conditions.

Main Methods:

A hybrid SNN-SVM model was proposed for Parkinson's disease detection.
Contrastive learning was employed to capture voice patterns across different languages.
The model was trained and tested using sustained vowel phonations (/a/, /e/, /i/, /o/, /u/ in Italian; /a/ in US-English and Spanish).

Main Results:

The hybrid SNN-SVM model achieved a peak accuracy of 93% for detecting Parkinson's disease using the vowel /o/ in the Italian dataset.
In cross-language testing, the model trained on Spanish and tested on US-English achieved an accuracy of 82.7%.
The proposed hybrid approach demonstrated enhanced accuracy in PD detection, effectively managing language variations.

Conclusions:

The hybrid SNN-SVM model offers an effective solution for Parkinson's disease detection in multilingual environments.
This approach shows potential for real-time application, overcoming limitations of language-specific voice analysis.
The study highlights the efficacy of contrastive learning in handling acoustic variations across languages for PD detection.