对声振荡的混因子分析
1Department of Electrical and Electronics Engineering, Antalya Bilim University, Antalya 07190, Turkey.
Entropy (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一种新方法来分析声运动,以使用语音记录来检测呼吸道感染. 研究结果表明,声相互作用随着COVID-19等呼吸道感染而发生变化,有助于基于语音的诊断.
科学领域:
- 生物医学工程 生物医学工程
- 声学分析 声学分析
- 医疗保健中的机器学习
背景情况:
- 语音分析是检测呼吸道感染的一种有希望的非侵入性方法.
- 了解声动态对于开发精确的基于语音的诊断工具至关重要.
- 当前的机器学习方法需要基于声运动的强有力的特征选择.
研究的目的:
- 开发一种方法来分析声运动中的复杂关系.
- 确定这些关系如何作为机器学习模型在呼吸道感染检测中的特征.
- 调查COVID-19等呼吸道感染对声动态的影响.
主要方法:
- 从母音声音录音中对声振荡 (位移和速度) 的联合多变量分析.
- 信息理论量,特别是转移的应用,以区分双变量和条件相互作用.
- 使用COVID-19数据集进行矢量自回归过程和验证的演示.
主要成果:
- 该方法成功地揭示了声运动中的二变和条件相互作用之间的差异.
- 条件数量提供了对影响声动态的混因素的见解.
- 对COVID-19数据的分析表明,由于感染,声振荡相互作用的变化.
结论:
- 拟议的方法增强了对机器学习的声音折叠运动关系的理解.
- 它可以帮助选择基于语音的诊断系统的最佳特性.
- 这种方法具有作为呼吸道感染的补充或早期检测工具的潜力.
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