从24小时的霍尔特心电图记录中基于人工智能的VO2max预测
Sarah Solbiati1, Maria Chiara Fiorentino2, Riccardo Bendandi1
1Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy.
NPJ microgravity
|November 22, 2025
概括
太空飞行会导致心血管衰竭,降低运动能力. 使用24小时心电图记录的机器学习模型可以预测最大有氧功率 (VO2max) 的下降,有助于在空间和临床环境中的监测.
科学领域:
- 心血管生理学心血管生理学
- 太空医学 太空医学
- 生物医学工程 生物医学工程
背景情况:
- 太空飞行诱导心血管 (CV) 脱水,影响心脏和血管由于微重力.
- 这种降低条件导致运动能力降低,特别是影响最大有氧功率 (VO2max).
- 目前在太空中监测心血管健康的方法有限.
研究的目的:
- 调查非运动24小时的霍尔特心电图记录是否可以预测微重力诱导的心脏衰竭.
- 开发机器学习模型,用于预测VO2最大值的恶化.
- 建立一个潜在的方法,用于在太空任务期间定期监测CV.
主要方法:
- 利用了从基线和卧床后休息期的头向下倾斜运动的数据.
- 开发和训练机器学习模型,使用来自Holter ECG数据的多个特征.
- 经过验证的模型可以预测解调后的VO2max.
主要成果:
- 机器学习模型对VO2max (RMSE<5mL/kg/min) 的预测准确度很好.
- 该方法在模拟微重力 (床上休息) 期间成功识别了VO2最大恶化.
- 这些发现突出了ECG衍生特征在评估心血管健康方面的潜力.
结论:
- 可以利用24小时的霍尔特心电图记录来预测心血管脱节.
- 对心电图数据的机器学习分析提供了一种可行的非运动方法来监测VO2max.
- 这种方法对太空中的宇航员健康管理和地球上的临床应用有重大影响.
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