使用基于深度学习的音频增强来提高自动呼吸声分类的稳定性和临床适用性:算法开发和验证
Jing-Tong Tzeng1, Jeng-Lin Li2, Huan-Yu Chen2
1College of Semiconductor Research, National Tsing Hua University, Hsinchu, Taiwan.
JMIR AI
|March 13, 2025
概括
将音频增强集成到深度学习模型中,可以显著提高噪音条件下的呼吸声分类精度. 这提高了诊断的敏感性,并建立了对临床应用的信任.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 深度学习在呼吸声分类方面表现有前途.
- 现实世界的噪音挑战了这些系统的临床部署.
- 使用背景噪音进行预测会削弱用户的信任.
研究的目的:
- 研究用于呼吸声分类的音频增强预处理.
- 提高系统的稳定性和临床适用性.
- 评估基于深度学习的音频增强效果.
主要方法:
- 实验了时间域和时间频率音频增强模型.
- 组合增强模块与多个分类模型.
- 对ICBHI和Formosa Archive数据集的噪声注入数据增强的性能进行比较.
- 与7名高级医生进行了医生验证.
主要成果:
- 在杂的场景中,ICBHI分类得分增加了21.88%,Formosa档案数据集增加了4.1%.
- 医生验证显示提高了效率,诊断信心和信任.
- 改进的音频工作流增加了11.61%的诊断灵敏度.
结论:
- 音频增强算法提高了呼吸声分类器的稳定性和临床实用性.
- 在杂的环境中,性能显著提高.
- 改进的系统促进了医疗专业人员之间更大的信任.
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