通过机器学习对心力衰竭患者的血液供应能力的基于脉冲波的评估
Sirui Wang1, Ryohei Ono2, Dandan Wu1
1Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
Biomedical engineering online
|January 19, 2024
概括
机器学习分析脉冲波,以非侵入性地评估心力衰竭 (HF) 患者的血液供应. 这种方法准确地预测了关键的心血管功能参数,提供了一个患者友好的监测工具.
科学领域:
- 心血管医学 心血管医学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 脉冲波分析提供了关于心血管系统 (CVS) 状况的见解.
- 心力衰竭 (HF) 的诊断和监测往往是昂贵和耗时的.
- 需要使用非侵入性方法来评估HF心脏功能.
研究的目的:
- 开发和验证一种机器学习 (ML) 方法,用于使用脉冲波数据对HF患者的血液供应能力进行非侵入性评估.
- 预测HF患者的关键心血管功能参数.
主要方法:
- 利用了来自237名高压患者的临床数据.
- 采用并优化了基于脉冲波数据集的两个ML网络.
- 使用统计分析,布兰德-阿尔特曼分析和错误函数分析验证了ML模型.
主要成果:
- 精确评估HF患者的血液供应能力.
- 能够快速预测五个心血管功能参数:LVEF,LVDd,LVDs,LAD和SpO2.
- 在 SpO2,LAD 和 LVDd 评估中实现的最大误差为 <15%.
结论:
- 基于脉冲波的ML分析显示了非侵入性HF评估的潜力.
- 这种方法可以促进患者友好的健康监测和恶化预防.
- 进一步的改进可以提高这种技术在心血管护理中的应用.
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