一种基于喘生理模型的新型数据增强技术,应用于呼吸道声音中的喘严重性管理
J Torre-Cruz1, F Canadas-Quesada1, R Cortina-Parajon2
1Department of Telecommunication Engineering. University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares (Jaen), 23700, Spain.
Computers in biology and medicine
|August 20, 2025
概括
使用新型数据增强技术生成合成喘息声音可以改善喘诊断的深度学习模型. 这种方法克服了数据的局限性,能够准确地检测喘严重程度,与真实患者数据相比,准确性损失最小.
科学领域:
- 生物医学工程 生物医学工程
- 呼吸系统医学 呼吸系统医学
- 人工智能的人工智能
背景情况:
- 喘息的声音对于诊断呼吸系统疾病,如喘至关重要.
- 目前的诊断模型由于有限,多样化,高质量的喘息声音数据集而扎.
- 挑战包括耗时的数据收集,患者的可用性和不常见的喘息事件.
研究的目的:
- 开发一种新的数据增强技术,用于生成合成喘息声音.
- 克服患者衍生数据和不一致的增强方法的局限性.
- 创建一个强大的学习工具,用于呼吸声分析和喘严重程度检测.
主要方法:
- 提出了一种基于喘生理学的新型数据增强技术.
- 该方法在数学上模拟喘声音的时间和光谱特征.
- 该技术被整合到一个医疗监督的应用程序中,用于诊断喘严重程度.
主要成果:
- 用合成数据训练的卷积神经网络 (CNN) 模型显示了与真实数据相比的竞争性表现 (精度下降<2.5%).
- 时间能量和振动分布是合成和真实声音相似性的关键参数.
- 数据增强方法有效地概括了现实世界喘声音的主要特征.
结论:
- 拟议的数据增强方法对于生成广泛的合成喘息数据是有效的.
- 合成数据可以用于呼吸声分析中的强大的深度学习模型.
- 开发的应用程序有助于加快高喘严重程度的医疗干预.
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