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相关概念视频

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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在多发性硬化症中通过自动语音分析检测疲劳.

Marcelo Dias1, Felix Dörr1, Susett Garthof2

  • 1ki:elements GmbH, Saarbrücken, Germany.

Frontiers in human neuroscience
|September 30, 2024
PubMed
概括
此摘要是机器生成的。

自动语音分析可以检测多发性硬化症 (MS) 患者的疲劳. 特定的语音模式,特别是来自叙事任务,提供了一个客观的方法来评估MS疲劳.

关键词:
自动化语音分析系统疲劳 疲劳 疲劳 疲劳 疲劳 疲劳机器学习是机器学习.多发性硬化症 (MS) 是一种疾病.演讲 演讲 演讲 演讲

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科学领域:

  • 神经学 神经学
  • 生物医学工程 生物医学工程
  • 语言病理学 语音病理学

背景情况:

  • 多发性硬化症 (MS) 是一种影响中枢神经系统的慢性神经炎症性疾病.
  • 疲劳是MS患者常见和衰弱的症状,严重影响日常生活.
  • 目前用于测量MS相关疲劳的方法具有挑战性,缺乏客观性.

研究的目的:

  • 评估自动语音分析在检测MS患者疲劳时的有效性.
  • 探索特定语音模式作为MS疲劳的客观生物标志物的潜力.

主要方法:

  • 多发性硬化症患者接受了临床评估和包括自由言语任务在内的综合语音协议.
  • 使用语音特征和认知分数训练了一个支持向量机器模型.
  • 来自图片描述任务的语言特征被分析为与疲劳的相关性.

主要成果:

  • 机器学习模型在使用语音特征和认知得分来检测疲劳时,实现了0.74的曲线下面面积 (AUC).
  • 仅仅从图片描述任务的语音特征就产生了0.68的AUC,这表明特定语音模式的实用性.
  • 认知疲劳与自由言论中的较低言语比率有显著的关联 (ρ = -0.283,p = 0.001).

结论:

  • 自动语音分析,特别是从单个叙事自由言论任务,提供了一个客观的,生态有效的,和低负担的方法来评估MS疲劳.
  • 语音分析工具在监测和管理多发性硬化症患者的疲劳方面显示出临床应用的前景.