通过使用域不变预处理和转移学习来提高声心图信号分类的跨域稳定性
1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
Computer methods and programs in biomedicine
|November 3, 2024
概括
这项研究通过开发强大的机器学习方法来增强使用心电图 (PCG) 信号的心血管疾病检测. 该方法提高了不同数据集的准确性,使自动心脏查更可靠.
科学领域:
- 生物医学信号处理
- 心血管诊断心血管诊断服务
- 医疗保健中的机器学习
背景情况:
- 声心图 (PCG) 信号分析为诊断心血管疾病提供了一种非侵入性方法.
- 目前用于PCG分析的机器学习 (ML) 方法因不同的数据采集设置而与不同数据集的性能变化作斗争.
- 这种变化对自动疾病检测系统的可靠性产生了重大影响.
研究的目的:
- 调查数据采集参数变化对来自不同数据库的PCG数据的影响.
- 开发基于PCG的强大的心血管疾病检测方法,适应跨数据集的变化.
- 增强自动心脏查系统的现实应用能力.
主要方法:
- 采用了域不变预处理,转移学习和域平衡变量跳跃片段选择 (DBVHFS) 的组合.
- 域不变预处理规范PCG信号以最大限度地减少听筒镜和环境变化.
- 转移学习使用预先训练的音频模型进行通用特征表示,DBVHFS确保了在所有领域中平衡的训练片段分布.
主要成果:
- 拟议的方法在六个独立的PhysioNet/CinC Challenge 2016 PCG数据库上进行了评估,使用一个离开一个数据集的交叉验证策略.
- 与现有方法相比,在未加权平均回忆中实现了5.92%的相对改善,在灵敏度方面达到17.71%.
- 在交叉数据集评估中表现出卓越的性能,突出显示了系统的稳定性.
结论:
- 开发的方法有效地解决来自不同来源的PCG数据的差异.
- 拟议的方法显示出在改善临床实践中自动心脏查系统的可靠性和实施方面的巨大潜力.
- 增强对数据变化的稳定性为人工智能在心血管诊断中更广泛采用铺平了道路.
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