Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

1.7K
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...
1.7K
Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

972
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...
972
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Neural Regulation01:37

Neural Regulation

43.1K
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.
43.1K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Total Synthesis and Antibacterial Evaluation of (-)- and (+)-<i>epi</i>-Perrottetinene.

ACS infectious diseases·2026
Same author

A community-based study of antibiotic consumption in an urban health training centre area of Ahmedabad city.

The Indian journal of medical research·2026
Same author

Role of dopamine in the development of impaired counterregulation and impaired awareness of hypoglycemia.

Cell reports. Medicine·2026
Same author

Challenges and developments in the manufacturing of high-concentration biotherapeutic monoclonal antibodies.

Advanced drug delivery reviews·2026
Same author

Sustainable Synthesis and Medicinal Profiling of Azo-Anchored Imidazo[4,5-b]Indole Scaffolds: DFT Studies, Electrochemical Sensing, and Pharmacokinetic Evaluation.

Archiv der Pharmazie·2026
Same author

Elucidating the potential of novel class biphenyl-phenyl acetate, IDD-AN-A1, an inhibitor targeting Isocitrate Lyase in Mycobacterium tuberculosis: A target to lead approach.

Bioorganic chemistry·2026

相关实验视频

Updated: Jan 14, 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

920

基于声格特征的帕金森病检测:一个合体学习方法

Megha Chakole1, Sanjay Dorle2, Rahul Agrawal3

  • 1Department, of Electronics and Telecommunication Engineering, Yeshwantaro Chavan College of Engineering, Maharashtra, India.

MethodsX
|October 27, 2025
PubMed
概括

机器学习,特别是渐变增强,可以有效地利用声学特征预测帕金森病 (PD). 这种方法有助于早期检测,并支持对越来越多的PD病例进行临床诊断.

关键词:
在AUC-ROC曲线上,机器学习分类器算法算法测量分析指标的分析.帕金森病是帕金森病的一种疾病.声乐的特点是声乐的特点.

更多相关视频

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

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

Published on: August 9, 2024

2.0K
Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
11:00

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

Published on: August 8, 2011

20.1K

相关实验视频

Last Updated: Jan 14, 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

920
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

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

Published on: August 9, 2024

2.0K
Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
11:00

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

Published on: August 8, 2011

20.1K

科学领域:

  • 神经学 神经学
  • 人工智能的人工智能

背景情况:

  • 帕金森病 (PD) 是一种进展性神经退行性疾病,影响中枢神经系统.
  • 全球PD病例在2019年超过了850万,突显了早期检测和干预的关键需求.

研究的目的:

  • 确定一种最佳的机器学习技术,用于早期预测帕金森病.
  • 为了评估各种机器学习算法的有效性,使用语音特征来检测PD.

主要方法:

  • 机器学习算法的比较,包括随机森林,K最近邻居,天真贝叶斯,梯度增强和XGBoost.
  • 基于性能指标的评估,如召回,日志损失和过度适应阻力.
  • 利用来自大规模数据集的声音特征进行PD预测.

主要成果:

  • 与其他算法相比,渐变增强显示出更高的性能.
  • 渐变增强模型实现了高回忆率,低日志损失,以及抗过的性能.
  • 声乐特征被确定为早期帕金森病检测的重要指标.

结论:

  • 机器学习,特别是渐变增强,在早期发现帕金森病方面显示出重大前景.
  • 语音生物标志物与机器学习相结合,可以增强诊断能力.
  • 这项研究通过为PD诊断和决策提供优化的机器学习技术,为医疗中心提供便利.