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Related Concept Videos

Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

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 which...
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

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 its...
Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

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 to...

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Related Experiment Video

Updated: Jun 12, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech

Arifa Zahir1, Jaehong Yu2, Jin-Sun Jun3

  • 1Department of Biomedical and Robotics Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea, 82 32-835-8677.

JMIR Medical Informatics
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model using voice analysis can detect Parkinson disease with high accuracy. Integrating speech recognition features significantly improves early detection and reduces false negatives, aiding noninvasive screening.

Keywords:
Parkinson diseaseautomatic speech recognitiondeep learningmultiview learningmultiview spectrogramvoice-based screening

Related Experiment Videos

Last Updated: Jun 12, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Speech Science

Background:

  • Parkinson disease often presents with early vocal impairments.
  • Developing noninvasive, scalable digital tools for early screening is crucial.

Purpose of the Study:

  • To propose a deep learning framework using multiview spectrograms and recognition-aware context for Parkinson disease detection from voice.
  • To evaluate the efficacy of integrating automatic speech recognition features with voice spectrograms.

Main Methods:

  • Collected voice recordings from 203 participants (121 Parkinson disease, 82 controls).
  • Utilized three spectrogram types (Mel, STFT, CQT) processed via parallel CNNs.
  • Fused spectrogram embeddings with a recognition ratio (RR) feature vector derived from ASR transcript agreement.

Main Results:

  • The multiview spectrogram network achieved 86.9% accuracy.
  • Incorporating the RR feature improved accuracy to 97.4%.
  • RR integration reduced the false negative rate by 84.5%, enhancing sensitivity.

Conclusions:

  • Combining multiview spectrogram learning with recognition-aware context significantly improves voice-based Parkinson disease classification.
  • The approach shows potential for noninvasive screening in structured settings.
  • Further validation in diverse real-world environments is necessary.