基于时间卷积神经网络的特征提取和异步通道信息融合方法,用于在声心图中检测心脏异常
Jae-Man Shin1, Seongyong Park2, Keewon Shin3
1Department of Anesthesiology and Pain Medicine, BK21 Project, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Computer methods and programs in biomedicine
|June 3, 2025
概括
这项研究引入了使用时卷积网络 (TCN) 来从心电图 (PCG) 信号准确检测儿科心脏异常的新框架,从而改善资源有限的环境中的查.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 听力是儿童心脏异常查的关键,特别是在资源有限的地区.
- 声心图 (PCG) 信号变化和区分声带来了临床挑战.
研究的目的:
- 开发一个强大的框架,用PCG信号检测儿科心脏异常.
- 为了解决信号变化和提高诊断准确性.
主要方法:
- 一个基于时间卷积网络 (TCN) 的框架,用于特征提取和信息融合.
- 在患者层面整合异步获取的PCG记录.
- 转移学习的应用,以克服领域适应挑战.
主要成果:
- 该框架在公开PCG数据集上表现出强的表现.
- 转移学习显著改善了外部验证的表现 (AUC 0.931±0.027,AUPRC 0.867±0.064).
- 结合数据集,提高了检测心脏异常的模型通用性.
结论:
- 拟议的框架为儿科心脏异常检测提供了灵活而准确的解决方案.
- 它可以容纳多通道,可变长度的PCG录音,适合低资源设置.
- 开源代码可用于更广泛的采用和研究.
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