脉冲波信号驱动的机器学习用于识别心力衰竭患者左心室扩大
Dandan Wu1, Ryohei Ono2, Sirui Wang1
1Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
Biomedical engineering online
|June 22, 2024
概括
机器学习模型使用脉冲波信号准确地识别心力衰竭患者的左心室扩大 (LVE). 这种非侵入性方法为诊断心脏重塑提供了一个有希望的工具.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 左心室扩大 (LVE) 是心脏重塑的关键指标,与心力衰竭 (HF) 和心律失常密切相关.
- 准确识别LVE对于管理HF和预防心脏不良事件至关重要.
研究的目的:
- 开发和验证基于机器学习 (ML) 的策略,用于使用脉冲波信号识别HF患者的LVE.
- 为了比较分类和回归ML模型在检测LVE时的性能.
主要方法:
- 从264名高频患者中创建了两个脉冲波数据集 (非LVE和LVE).
- 里埃序列分析验证了数据集频率差异.
- 一个加权的随机森林模型进行了二元分类,一个密集连接的卷积网络通过回归估计了左心室扩张直径指数 (LVDdI).
主要成果:
- 该分类模型实现了0.91准确度和0.93AUC-ROC.
- 回归模型的准确度为0.88和AUC-ROC为0.89.
- 两种ML模型都在识别HF患者的LVE方面表现出高准确度.
结论:
- 拟议的ML方法有效地使用脉冲波信号识别HF患者的LVE.
- 这一策略显示出临床应用的潜力,为诊断心室重塑提供了强大的工具.
- 该研究强调了基于ML的非侵入性LVE检测的可行性.
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