有效的多视图融合和灵活适应视图缺失的心血管系统信号
Qihan Hu1, Daomiao Wang1, Hong Wu1
1Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.
概括
这项研究引入了新的视图中心变压器 (VCT) 和多任务蒙面自动编码器 (M2AE) 模型,用于心血管系统 (CVS) 多视图融合 (MVF). 这些模型有效地处理缺失的数据,并提高心血管健康监测的准确性.
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
- 心血管系统监测 监测心血管系统
背景情况:
- 深度学习和传感器的采用使心血管系统 (CVS) 信号的自动多视图融合 (MVF) 成为可能.
- 现有的MVF模型经常忽略异步心血管事件,视图异质性,导致"灾难性视图混".
- 现实世界的数据经常呈现不完整的观点,这是当前MVP研究未能充分解决的挑战.
研究的目的:
- 开发先进的MVF模型,解决当前架构的局限性,特别是关于异步数据和缺失视图的局限性.
- 为 MVF 模型引入有效的培训策略,以实现全面的表示.
- 系统地定义和解决心血管监测的MVF中缺失视图问题.
主要方法:
- 拟议的视图中心变压器 (VCT) 和多任务掩盖自动编码器 (M2AE),以强调个别视图的重要性,并利用未标记的数据.
- 引入了预训练的MVF模型的快速技术,以适应各种缺失视图场景,最小的微调.
- 进行了对心房的检测,血压估计和睡眠分期的实验.
主要成果:
- 与当前的方法相比,VCT和M2AE模型显示出优越的融合表示.
- 快速技术在<3%的微调下实现了对缺失视图的显著弹性,避免了完全的重新训练.
- 实验结果证实了该方法在典型的心血管健康监测任务中的有效性.
结论:
- 拟议的VCT和M2AE模型为MVF在心血管监测中提供了一个强大的解决方案,有效地处理数据异质性和缺失的观点.
- 提示技术提供了一种高效和有效的方式,可以将预训练的MVF模型适应失踪视图场景.
- 这些进步具有实时心血管健康监测中的实际应用的巨大潜力.
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