语音分析和神经网络作为肺部疾病患者的临床决策支持系统
Kamilla A Bringel1, Davi C M G Leone1, João Vitor L de C Firmino2
1Medical Science Center, Federal University of Paraiba, João Pessoa, Paraiba, Brazil.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
人工神经网络 (ANN) 可以通过分析语音模式来准确检测肺部疾病. 这种方法提供了一种有希望的,非侵入性的方法来评估呼吸道疾病,如呼吸障碍.
科学领域:
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
- 肺部病理学 肺部病理学
背景情况:
- 肺部疾病显著影响声乐特征.
- 早期发现呼吸道疾病对于有效管理至关重要.
- 需要非侵入性诊断工具来补充传统方法.
研究的目的:
- 分析肺部疾病患者与健康个体的语音模式.
- 开发人工神经网络 (ANN) 来评估呼吸障碍.
- 为了评估语音分析对肺部疾病的诊断准确性.
主要方法:
- 一项横截面的前性试点研究,比较肺部疾病患者和健康对照组.
- 在受控环境中进行语音录音.
- 使用统计分析,快速里埃变形,离散波形变形和Mel-Cepstral分析进行特征提取.
- 数据增强和ANN用于模式检测的应用.
主要成果:
- 使用ANN与光谱分析技术实现了高诊断准确度.
- 在从声音分类肺部疾病方面,男性的准确率为89%,女性的准确率为87.6%.
- 在患者和对照组之间检测到声音模式的显著差异.
结论:
- 使用ANN和光谱技术进行语音分析是诊断肺部疾病的高度准确方法.
- 这种方法显示了对呼吸道疾病的有效和非侵入性评估的潜力.
- 进一步的研究可以在更大,更多样化的群体中验证这些发现.
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